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Agriculture, Volume 14, Issue 2 (February 2024) – 167 articles

Cover Story (view full-size image): Understanding the assimilation of P through APases in the plant–soil/plant–microbiota ecosystem can be crucial for crop productivity and yields. This review qualitatively underscores the significance of APases in P uptake in agroecosystems and their role in the global P cycle. Observable changes in APase activity can be attributed to soil biophysicochemical properties, agricultural management practices, pollutants, and climate change. Although the selected studies cannot produce a meaningful summary estimate of the effect of more than two factors, the information obtained will enable us to manage agricultural systems to promote the capabilities of plants and associated microorganisms to assimilate nutrients and understand microbial-mediated processes and the dynamics of soil health. View this paper
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22 pages, 15934 KiB  
Article
Development and Optimization of an Offset Spiral Tooth Fertilizer Discharge Device
by Longyu Fang, Wenwu Yang, Xiwen Luo, Han Guo, Shiyu Song, Qinghai Liu, Haoyang Xie, Weiman Chen, Jianxin Lu, Zhixiang Peng and Guanjiong Li
Agriculture 2024, 14(2), 329; https://doi.org/10.3390/agriculture14020329 - 19 Feb 2024
Cited by 1 | Viewed by 1535
Abstract
Due to factors such as a small amount of fertilizer application during rice topdressing and slow machine speed, the ordinary fertilizer discharge device fails to distribute the fertilizer uniformly and accurately as required, making it difficult to meet the needs of precise rice [...] Read more.
Due to factors such as a small amount of fertilizer application during rice topdressing and slow machine speed, the ordinary fertilizer discharge device fails to distribute the fertilizer uniformly and accurately as required, making it difficult to meet the needs of precise rice topdressing. This research focuses on the development of an offset spiral tooth fertilizer discharge device suitable for rice topdressing. The study analyzes the amount of fertilizer discharged in one cycle, the fertilizer force, and the motion of the fertilizer particles. In order to enhance the uniformity of the fertilizer discharge device at a low speed and small volume, the discrete element method is employed to conduct experimental research on the key structural parameters that affect the one-cycle amount of discharged fertilizer and the dynamics of the fertilizer discharge device. Through single-factor tests, it was found that the angle, height, number of spiral teeth, and diameter of the fertilizer discharge wheel are closely associated with the fertilizer discharge performance. To further investigate the impact of these four parameters on the fertilizer discharge performance, a regression combination test of the four factors is conducted based on the range optimized by the single-factor tests. A multi-objective mathematical model, considering the key parameters of fertilizer uniformity coefficient, one-cycle amount of fertilizer, and fertilizer discharge torque, is established at three speeds: 20, 55, and 90 rpm. The response surface method is utilized to analyze the influence of the interaction factors on the fertilizer discharge performance. The optimal combination of the key structural parameters was determined as follows: spiral tooth angle of 35.42°, tooth height of 9.02 mm, discharge wheel diameter of 57.43 mm, and tooth amount of 9.37. The bench test results of the device, using the optimal parameter combination and a fertilizer discharge speed of 0–90 rpm, were obtained for four commonly used rice fertilizers. The maximum variation coefficient of fertilizer discharge was found to be 10.42%. The one-cycle amount of fertilizer discharge was measured to be 19.82 ± 0.72 (A Kang), 17.20 ± 0.69 (Ba Tian), 20.34 ± 0.54 (Yaran), and 14.51 ± 0.44 (granular urea). The fertilizer discharge torque remained stable. These results indicate that the achieved optimization meets the precise fertilizer application requirements and can provide technical support for precise topdressing operations. Full article
(This article belongs to the Special Issue Agricultural Machinery Design and Agricultural Engineering)
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<p>Structure of the fertilizer equipment. (<b>a</b>) Rice topdressing fertilizer distribution device; (<b>b</b>) offset spiral tooth fertilizer device unit.</p>
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<p>Kinematic analysis of fertilizer particles: (<b>a</b>) force analysis; (<b>b</b>) motion analysis.</p>
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<p>Fertilizer parameters. (<b>a</b>) Distribution of the density and angle of repose; (<b>b</b>) probability distribution map of the fertilizer particle size.</p>
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<p>Measurement and simulation of the angle of repose. (<b>a</b>) A Kang; (<b>b</b>) Ba Tian; (<b>c</b>) Yaran; (<b>d</b>) granular urea; (<b>e</b>) simulated.</p>
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<p>Schematic diagram of the simulation model and data collection. (<b>a</b>) simulation model. (<b>b</b>) Data collection boxes.</p>
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<p>Level setting of single-factor test factors. (<b>a</b>) Angle, (<b>b</b>) height, (<b>c</b>) diameter of the fertilizer discharge wheel, and (<b>d</b>) number of teeth for the fertilizer discharge wheel.</p>
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<p>Single-factor analysis of the spiral angle of the tooth. (<b>a</b>) One-cycle amount of fertilizer. (<b>b</b>) Uniformity of discharge. (<b>c</b>) Fertilizer discharge torque.</p>
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<p>Single-factor analysis of the height of the tooth. (<b>a</b>) One-cycle amount of fertilizer. (<b>b</b>) Uniformity of discharge. (<b>c</b>) Fertilizer discharge torque.</p>
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<p>Single-factor analysis of the diameter of the fertilizer discharge wheel. (<b>a</b>) One-cycle amount of fertilizer. (<b>b</b>) Uniformity of discharge. (<b>c</b>) Fertilizer discharge torque.</p>
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<p>Single-factor analysis of the number of teeth of the fertilizer discharge wheel. (<b>a</b>) One-cycle amount of fertilizer. (<b>b</b>) Uniformity of discharge. (<b>c</b>) Fertilizer discharge torque.</p>
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<p>Influence of the interaction factors on the variable coefficient. (<b>a</b>) <span class="html-italic">VC</span><sub>(20 rpm)</sub>. (<b>b</b>) <span class="html-italic">VC</span><sub>(55 rpm)</sub>. (<b>c</b>) <span class="html-italic">VC</span><sub>(55 rpm)</sub>.</p>
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<p>Influence of the interaction factors on the one-cycle amount of fertilizer between the spiral angel and diameter of the fertilizer wheel. (<b>a</b>) <span class="html-italic">Q</span><sub>(20 rpm)</sub>. (<b>b</b>) <span class="html-italic">Q</span><sub>(55 rpm)</sub>. (<b>c</b>) <span class="html-italic">Q</span><sub>(90 rpm)</sub>.</p>
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<p>Influence of the interaction factors on the one-cycle amount of fertilizer between the height and diameter of the fertilizer wheel. (<b>a</b>) <span class="html-italic">Q</span><sub>(20 rpm)</sub>. (<b>b</b>) <span class="html-italic">Q</span><sub>(55 rpm)</sub>. (<b>c</b>) <span class="html-italic">Q</span><sub>(90 rpm)</sub>.</p>
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<p>Verification test.</p>
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<p>Test results of the coefficient of variation at different speeds.</p>
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<p>Results of the bench test. (<b>a</b>) Uniformity of discharge. (<b>b</b>) One-cycle amount of fertilizer.</p>
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<p>Half-violin diagram of the fertilizer discharge torque during the bench test.</p>
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19 pages, 1447 KiB  
Article
Effect of Genotype, Environment, and Their Interaction on the Antioxidant Properties of Durum Wheat: Impact of Nitrogen Fertilization and Sowing Time
by Stergios Melios, Elissavet Ninou, Maria Irakli, Nektaria Tsivelika, Iosif Sistanis, Fokion Papathanasiou, Spyros Didos, Kyriaki Zinoviadou, Haralabos Christos Karantonis, Anagnostis Argiriou and Ioannis Mylonas
Agriculture 2024, 14(2), 328; https://doi.org/10.3390/agriculture14020328 - 19 Feb 2024
Cited by 1 | Viewed by 1475
Abstract
In this study, the influence of genotype (G), environment (E), and their interaction (G × E) on the content of total free phenolic compounds (TPC) and the antioxidant capacity (AC) was investigated, using sixteen durum wheat genotypes cultivated under seven crop management systems [...] Read more.
In this study, the influence of genotype (G), environment (E), and their interaction (G × E) on the content of total free phenolic compounds (TPC) and the antioxidant capacity (AC) was investigated, using sixteen durum wheat genotypes cultivated under seven crop management systems in Mediterranean environments. Possible correlations between TPC and AC with protein content (PC) and vitreous kernel percentage (VKP) were examined. Gs that exhibited stability across diverse conditions were studied through a comprehensive exploration of G × E interaction using a GGE biplot, Pi, and 𝘒R. The results indicated significant impacts of E, G, and G × E on both TPC and AC. Across E, the mean values of G for TPC, ABTS (2’-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid), DPPH (2,2-diphenyl-1-picrylhydrazyl), and FRAP (ferric reducing antioxidant power) values were 48.8 mg Trolox equivalents (TE)/100 g, 121.3 mg TE/100 g, 23.0 mg TE/100 g, and 88.4 mg TE/100 g, respectively. E, subjected to splitting top-dressing N fertilization, consistently showed low values, while the late-sowing ones possessed high values. Organic crop management maintained a stable position in the middle across all measurements. The predominant influence was attributed to G × E, as indicated by the order G × E > E > G for ABTS, DPPH, and FRAP, while for TPC, it was E > G × E > G. For TPC, the superior Gs included G5, G7 and G10, for ABTS included G3, G5 and G7, and for protein included G1, G9, and G16. G7 and G5 had a high presence of frequency, with G7 being the closest genotype to the ideal for both TPC and ABTS. These results suggest that the sowing time, nitrogen fertilization, and application method significantly impact the various antioxidant properties of durum wheat. This study holds significant importance as it represents one of the few comprehensive explorations of the impact of various Es, Gs, and their interactions on the TPC and AC in durum wheat, with a special emphasis on crop management and superior Gs possessing stable and high TPC and AC among them, explored by GGE biplot, Pi and 𝘒R. Further experimentation, considering the effect of the cultivation year, is necessary, to establish more robust and stable conclusions. Full article
(This article belongs to the Section Crop Production)
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<p>Genotype and genotype by environment (GGE) comparison biplot of sixteen genotypes evaluated in seven environments for TPC (<b>left</b>), ABTS (<b>center</b>), and protein (<b>right</b>).</p>
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21 pages, 16074 KiB  
Article
Wheat Teacher: A One-Stage Anchor-Based Semi-Supervised Wheat Head Detector Utilizing Pseudo-Labeling and Consistency Regularization Methods
by Rui Zhang, Mingwei Yao, Zijie Qiu, Lizhuo Zhang, Wei Li and Yue Shen
Agriculture 2024, 14(2), 327; https://doi.org/10.3390/agriculture14020327 - 19 Feb 2024
Cited by 1 | Viewed by 1355
Abstract
Wheat breeding heavily relies on the observation of various traits during the wheat growth process. Among all traits, wheat head density stands out as a particularly crucial characteristic. Despite the realization of high-throughput phenotypic data collection for wheat, the development of efficient and [...] Read more.
Wheat breeding heavily relies on the observation of various traits during the wheat growth process. Among all traits, wheat head density stands out as a particularly crucial characteristic. Despite the realization of high-throughput phenotypic data collection for wheat, the development of efficient and robust models for extracting traits from raw data remains a significant challenge. Numerous fully supervised target detection algorithms have been employed to address the wheat head detection problem. However, constrained by the exorbitant cost of dataset creation, especially the manual annotation cost, fully supervised target detection algorithms struggle to unleash their full potential. Semi-supervised training methods can leverage unlabeled data to enhance model performance, addressing the issue of insufficient labeled data. This paper introduces a one-stage anchor-based semi-supervised wheat head detector, named “Wheat Teacher”, which combines two semi-supervised methods, pseudo-labeling, and consistency regularization. Furthermore, two novel dynamic threshold components, Pseudo-label Dynamic Allocator and Loss Dynamic Threshold, are designed specifically for wheat head detection scenarios to allocate pseudo-labels and filter losses. We conducted detailed experiments on the largest wheat head public dataset, GWHD2021. Compared with various types of detectors, Wheat Teacher achieved a mAP0.5 of 92.8% with only 20% labeled data. This result surpassed the test outcomes of two fully supervised object detection models trained with 100% labeled data, and the difference with the other two fully supervised models trained with 100% labeled data was within 1%. Moreover, Wheat Teacher exhibits improvements of 2.1%, 3.6%, 5.1%, 37.7%, and 25.8% in mAP0.5 under different labeled data usage ratios of 20%, 10%, 5%, 2%, and 1%, respectively, validating the effectiveness of our semi-supervised approach. These experiments demonstrate the significant potential of Wheat Teacher in wheat head detection. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)
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<p>The pipeline of Wheat Teacher.</p>
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<p>The network structure of YOLOv5.</p>
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<p>Some example images in the GWHD2021.</p>
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<p>Graphs depicting loss curves.</p>
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<p>Graphs depicting mAP0.5 curves.</p>
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<p>Graphs depicting mAP0.5:0.95 curves.</p>
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<p>Graphs depicting F1 curves.</p>
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<p>Graphs depicting PR curves.</p>
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<p>Differences in predictions under four different situations. The red box represents the wheat head. GT represents the number of wheat heads ground truth, and Pred represents the number of wheat heads predicted by the model.</p>
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18 pages, 2827 KiB  
Article
Effects and Underlying Mechanisms of Rice-Paddy-Upland Rotation Combined with Bacterial Fertilizer for the Abatement of Radix pseudostellariae Continuous Cropping Obstacles
by Sheng Lin, Yuanyuan Yang, Ting Chen, Yanyang Jiao, Juan Yang, Zhaoying Cai and Wenxiong Lin
Agriculture 2024, 14(2), 326; https://doi.org/10.3390/agriculture14020326 - 19 Feb 2024
Viewed by 1306
Abstract
Radix pseudostellariae is one of the well-known genuine medicinal herbs in Fujian province, China. However, the continuous cropping obstacles with respect to R. pseudostellariae have seriously affected the sustainable utilization of medicinal resources and the development of related industrial systems. The occurrence of [...] Read more.
Radix pseudostellariae is one of the well-known genuine medicinal herbs in Fujian province, China. However, the continuous cropping obstacles with respect to R. pseudostellariae have seriously affected the sustainable utilization of medicinal resources and the development of related industrial systems. The occurrence of continuous cropping obstacles is a comprehensive effect of multiple deteriorating biological and abiotic factors in the rhizosphere soil. Therefore, intensive ecological methods have been the key to abating such obstacles. In this study, four treatments were set up, i.e., fallow (RP-F), fallow + bacterial fertilizer (RP-F-BF), rice-paddy-upland rotation (RP-R), and rice-paddy-upland rotation + bacterial fertilizer (RP-R-BF), during the interval between two plantings of R. pseudostellariae, with a newly planted (NP) treatment as the control. The results show that the yield of R. pseudostellariae under the RP-F treatment decreased by 46.25% compared to the NP treatment. Compared with the RP-F treatment, the yields of the RP-F-BF, RP-R, and RP-R-BF treatments significantly increased by 14.11%, 27.79%, and 62.51%, respectively. The medicinal quality of R. pseudostellariae treated with RP-R-BF was superior to that achieved with the other treatments, with the total saponin and polysaccharide contents increasing by 8.54% and 27.23%, respectively, compared to the RP-F treatment. The ecological intensive treatment of RP-R-BF significantly increased the soil pH, content of organic matter, abundance of beneficial microbial populations, and soil enzyme activity, thus remediating the deteriorating environment of continuous cropping soil. On this basis, the ecological intensive treatment RP-R-BF significantly increased the activity of protective enzymes and the expression levels of genes related to disease and stress resistance in leaves and root tubers. Redundancy and Pearson correlation analyses indicated that rice-paddy-upland rotation improved the soil structure, promoted the growth of eutrophic r-strategy bacterial communities, enhanced compound oxidation and reduction, broke the relationship between the deteriorating environment and harmful biological factors, and eventually weakened the intensity of harmful factors. The subsequent application of bacterial fertilizer improved the beneficial biological and abiotic factors, activated various ecological functions of the soil, enhanced the ecological relationship between various biological and abiotic factors, and reduced the stress intensity of R. pseudostellariae, thereby improving its disease and stress resistance, and ultimately reflecting the recovery of yield and quality. The results indirectly prove that the intensive ecological amelioration of the soil environment was the main factor for the yield recovery of R. pseudostellariae under continuous cropping. Full article
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<p>Effects of different treatments on yield and quality of <span class="html-italic">R. pseudostellariae.</span> NP: the newly planting <span class="html-italic">R. pseudostellariae</span>; RP-F: the keeping soil fallow after harvesting <span class="html-italic">R. pseudostellariae</span>; RP-F-BF: applying bio-microbial fertilizer in fallow soil before the next planting period of <span class="html-italic">R. pseudostellariae</span>; RP-R: <span class="html-italic">R. pseudostellariae</span> rotated with rice; RP-R-BF: applying bio-microbial fertilizer in the soil after harvesting the rice rotated with <span class="html-italic">R. pseudostellariae</span>. Different lowercase letters indicate significant differences between different treatments at <span class="html-italic">p</span> &lt; 0.05. (<b>a</b>) Root diameter of <span class="html-italic">R. pseudostellariae</span> under different treatments; (<b>b</b>) content of total saponin in root tubers of <span class="html-italic">R. pseudostellariae</span>; (<b>c</b>) content of polysaccharide in root tubers of <span class="html-italic">R. pseudostellariae</span>.</p>
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<p>Antioxidant enzyme activities in the leaves of continuously monocultured <span class="html-italic">R. pseudostellariae</span> under different treatments. Treatments are the same as those given in <a href="#agriculture-14-00326-f001" class="html-fig">Figure 1</a>. SS: the seedling stage; EE: the early expanding stage of tuber roots; ME: the middle expanding stage of tuber roots; LE: the late expanding stage of tuber roots. (<b>a</b>) Content of MDA in the leaves of <span class="html-italic">R. pseudostellariae</span>; (<b>b</b>) POD activity in the leaves of <span class="html-italic">R. pseudostellariae</span>; (<b>c</b>) SOD activity in the leaves of <span class="html-italic">R. pseudostellariae</span>; (<b>d</b>) CAT activity in the leaves of <span class="html-italic">R. pseudostellariae</span>.</p>
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<p>Antioxidant enzyme activities in the tuber roots of continuously monocultured <span class="html-italic">R. pseudostellariae</span> under different treatments. Treatments are the same as those given in <a href="#agriculture-14-00326-f001" class="html-fig">Figure 1</a>. EE: the early expanding stage of tuber roots; ME: the middle expanding stage of tuber roots; LE: the late expanding stage of tuber roots. (<b>a</b>) Content of MDA in root tubers of <span class="html-italic">R. pseudostellariae</span>; (<b>b</b>) POD activity in root tubers of <span class="html-italic">R. pseudostellariae</span>; (<b>c</b>) SOD activity in root tubers of <span class="html-italic">R. pseudostellariae</span>; (<b>d</b>) CAT activity in root tubers of <span class="html-italic">R. pseudostellariae</span>.</p>
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<p>Soil pH value and organic content under different treatments. Treatments are the same as those given in <a href="#agriculture-14-00326-f001" class="html-fig">Figure 1</a>. BT: before treatments; BP: before the planting of second crop; SS: the seedling stage; EE: the early expanding stage of tuber roots; ME: the middle expanding stage of tuber roots; LE: the late expanding stage of tuber roots. Different lowercase letters indicate significant differences between different treatments at <span class="html-italic">p</span> &lt; 0.05. (<b>a</b>) Soil pH value under different treatments; (<b>b</b>) soil organic content under different treatments.</p>
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<p>Soil nitrogen, phosphorus, and potassium contents of <span class="html-italic">R. pseudostellariae</span> under different treatments. Treatments are the same as those given in <a href="#agriculture-14-00326-f001" class="html-fig">Figure 1</a>. SS: the seedling stage; EE: the early expanding stage of tuber roots; ME: the middle expanding stage of tuber roots; LE: the late expanding stage of tuber roots. Different lowercase letters indicate significant differences between different treatments at <span class="html-italic">p</span> &lt; 0.05. (<b>a</b>) Soil-available nitrogen under different treatments; (<b>b</b>) soil-available phosphorus under different treatments; (<b>c</b>) soil-available potassium under different treatments; (<b>d</b>) total soil nitrogen under different treatments; (<b>e</b>) total soil phosphorus under different treatments; (<b>f</b>) total soil potassium under different treatments.</p>
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<p>Analysis of r-K survival strategies of rhizosphere soil microorganisms under different soil intensification treatments. Treatments are the same as those given in <a href="#agriculture-14-00326-f001" class="html-fig">Figure 1</a>. (<b>a</b>) The proportion of species with different ribosome RNA operon copy numbers; (<b>b</b>) shows the proportion of r-strategy bacteria and K-strategy bacteria under different treatments; (<b>c</b>) shows the relative abundance of r-strategy bacteria, K-strategy bacteria, and r-K intermediate strategy bacteria under different treatments. Different lowercase letters indicate significant differences between different treatments at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Redundancy analysis between microbial communities and environmental factors in the rhizosphere soil of <span class="html-italic">R. pseudostellariae</span> under different treatments: (<b>a</b>) showed the redundancy analysis between fungal communities and environmental factors, and (<b>b</b>) showed the redundancy analysis between bacterial communities and environmental factors.</p>
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<p>The correlation analysis demonstrated the relationships between soil environmental factors and microbial communities. A: <span class="html-italic">Acidobacterium</span>, <span class="html-italic">Acidobacteria</span>, <span class="html-italic">Actinobacterium</span>, <span class="html-italic">Actinobacteria</span>; B: <span class="html-italic">Bacillus</span>, <span class="html-italic">Pseudomonas</span>, <span class="html-italic">Burkholderia-Paraburkholderia</span>, <span class="html-italic">Streptomyces</span>, <span class="html-italic">Penicillium</span>, <span class="html-italic">Trichoderma</span>, <span class="html-italic">Glomeromycota</span>; C: <span class="html-italic">Cellulomonas</span>; D: <span class="html-italic">Fusarium</span>, <span class="html-italic">Talaromyces</span>, <span class="html-italic">Aspergillus</span>, <span class="html-italic">Didymella</span>; E: <span class="html-italic">Rhizobacter</span>, <span class="html-italic">Nitrospira</span>, <span class="html-italic">Nitrosospira</span>, <span class="html-italic">Nitrospirae</span>.</p>
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21 pages, 10312 KiB  
Article
Numerical Simulation and Experimental Study of Corn Straw Grinding Process Based on Computational Fluid Dynamics–Discrete Element Method
by Xin Wang, Haiqing Tian, Ziqing Xiao, Kai Zhao, Dapeng Li and Di Wang
Agriculture 2024, 14(2), 325; https://doi.org/10.3390/agriculture14020325 - 18 Feb 2024
Cited by 1 | Viewed by 1326
Abstract
To improve the operational efficiency of a hammer mill and delve into a high-efficiency, energy-saving grinding mechanism, the crucial parameters influencing the grinding of corn straw were identified as the spindle speed, hammer–sieve gap, and sieve pore diameter. According to the force analysis [...] Read more.
To improve the operational efficiency of a hammer mill and delve into a high-efficiency, energy-saving grinding mechanism, the crucial parameters influencing the grinding of corn straw were identified as the spindle speed, hammer–sieve gap, and sieve pore diameter. According to the force analysis and kinematics analysis, the key factors affecting corn straw grinding were the spindle speed, the hammer–sieve gap, and the sieve pore diameter. The grinding process of corn straw was studied using computational fluid dynamics (CFDs) and the discrete element method (DEM) gas–solid coupling numerical simulation and experiment. The numerical simulation results showed that with the growth of time, the higher the spindle speed, the faster the bonds broke in each part, and the higher the grinding efficiency. When the energy loss of the hammer component was in the range of 985.6~1312.2 J, and the total collision force of the corn straw was greater than 47,032.5 N, the straw grinding effect was better, and the per kW·h yield was higher. The experimental results showed that the optimum combination of operating parameters was a spindle speed of 2625 r/min, a hammer-screen gap of 14 mm, and a sieve pore diameter of 8 mm. Finally, the CFD–DEM gas–solid coupling numerical simulation validation tests were performed based on the optimal combination of the operating parameters. The results showed that the energy loss of the hammer component was 1189.5 J, and the total collision force of the corn straw was 49,523.5 N, both of which were within the range of better results in terms of numerical simulation. Thus, the CFD–DEM gas–solid coupling numerical simulation could accurately predict the corn straw grinding process. This study provides a theoretical basis for improving a hammer mill’s key components and grinding performance. Meanwhile, the proposed gas–solid two-phase flow method provided theoretical references for other research in agricultural machinery. Full article
(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)
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<p>Three-dimensional model and sample machine of CPS-420 hammer mill: 1. upper feeding hopper; 2. sieve; 3. lower feeding hopper; 4. straw knife; 5. outlet; 6. frame; 7. hammer; 8. motor.</p>
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<p>Corn straw grinding process. (The location and grinding of the straw marked by the red square).</p>
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<p>Stress analysis diagram of corn straw.</p>
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<p>Motion analysis diagram of the corn straw unit.</p>
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<p>Force analysis diagram of the corn straw unit.</p>
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<p>Simplified model.</p>
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<p>Grid division.</p>
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<p>Numerical simulation model of corn straw: 1. inner pulp particles; 2. node particles; 3. outer skin particles.</p>
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<p>Diagram of bonds.</p>
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<p>Particle replacement.</p>
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<p>Test process.</p>
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<p>Velocity variation in the flow field inside the grinding chamber at different spindle speeds.</p>
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<p>Variation in pressure field inside the grinding chamber at different spindle speeds.</p>
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<p>The results of different spindle speeds on the number of bonds over time.</p>
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<p>The results of the variation in the energy loss of the hammer components with spindle speeds.</p>
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<p>The results of the variation in the energy loss of the hammer components with the hammer–sieve gap.</p>
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<p>The results of the energy loss of the hammer components with the change in sieve pore diameter.</p>
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<p>The vector diagram of straw collision force at different spindle speeds.</p>
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<p>Numerical simulation results of straw collision force variation with spindle speeds.</p>
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<p>Test results.</p>
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16 pages, 4958 KiB  
Article
Vibrational Dynamics of Rice Precision Hole Seeders and Their Impact on Seed Dispensation Efficacy
by Dongyang Yu, Feihu Peng, Zhihao Zeng, Minghua Zhang, Wenwu Yang, Ying Zang, Jianfei He, Yichen Huang, Yuguang Wu, Wenneng Zhong, Ziyou Guo, Jiawen Liu, Guanjiong Li, Xingmou Qin and Zaiman Wang
Agriculture 2024, 14(2), 324; https://doi.org/10.3390/agriculture14020324 - 18 Feb 2024
Cited by 4 | Viewed by 1379
Abstract
This investigation considered the effects of both internal and external excitation vibrations on the efficacy of the seed dispenser in a rice precision hole seeder. Through comprehensive field tests, we analyzed vibrational characteristics during direct seeder operations and established a vibration seeding test [...] Read more.
This investigation considered the effects of both internal and external excitation vibrations on the efficacy of the seed dispenser in a rice precision hole seeder. Through comprehensive field tests, we analyzed vibrational characteristics during direct seeder operations and established a vibration seeding test bed for systematic examination of these effects. Time-domain analysis of the vibration data revealed a predominantly vertical vibration direction, with notably higher levels in sandy loam soil compared to clay loam. A correlation was observed between increased engine size and rotary ploughing speeds, as well as forward speed and elevated vibration amplitudes. Frequency domain analysis pinpointed the primary vibration frequency of the machinery within the 0–170 Hz range, remaining consistent across different operating conditions. Crucially, bench test results indicated that seeding accuracy and dispersion were significantly influenced by vibration frequencies, particularly within the 70–130 Hz range, where a decrease in accuracy and increase in dispersion were noted. A regression model suggested a complex, non-linear relationship between seeding performance and vibration frequency. These insights highlight the necessity for a robust mechanism to effectively address these vibrational impacts. This study paves the way for enhancing the operational efficiency of the rice precision hole seeder, aiming to achieve the design goals of minimized vibrations in the paddy power chassis. Full article
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<p>The structure of the rice precision hole seeder. 1. Furrowing device. 2. Seed distributor. 3. Slide plate. 4. Horizontal profiling mechanism. 5. Hitching bracket. 6. PTO power rotation unit. 7. Elevation profiling mechanism. 8. Frame. 9. Lateral panels.</p>
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<p>Seeder vibration test under different field soil-type conditions: (<b>a</b>) clay loam field, (<b>b</b>) sandy loam field.</p>
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<p>Acceleration sensor test point location. 1. First row of seeders. 2. Fifth row of seeders.</p>
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<p>Vibration time-domain signals from the fifth seed discharger of a direct-driven machine operating at a speed of 1.13 m/s in a clay loam soil field: (<b>a</b>) forward direction (X-channel), (<b>b</b>) vertical direction (Y channel), and (<b>c</b>) swing direction (Z channel).</p>
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<p>Root mean square values for vibration acceleration were obtained for the initial and fifth seed rowers of the direct seeding equipment: (<b>a</b>) first row of seeders, (<b>b</b>) fifth row of seeders.</p>
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<p>Three-dimensional spectrograms of acceleration for different seed dischargers operating in a clay loam field: (<b>a</b>) first row of seeders, (<b>b</b>) fifth row of seeders.</p>
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<p>Three-dimensional spectrograms of acceleration for different seed dischargers operating in a sandy loam field: (<b>a</b>) first row of seeders, (<b>b</b>) fifth row of seeders.</p>
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<p>Seeder vibration test stand. 1. Six-degree spatial vibration tester. 2. Computer. 3. Conveyor controller. 4. Conveyor belts. 5. Fill light. 6. Image acquisition camera. 7. Seeder holder. 8. Seeder. 9. Seeder sprocket. 10. Motor controller. 11. Electrical machinery.</p>
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<p>Correspondence between seeding slot width and image pixels.</p>
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<p>Relationship between seeding accuracy and dispersion curves of seed dischargers at different frequencies.</p>
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<p>Regression analysis between vibration frequency of seed displacer and seeding accuracy and dispersion: (<b>a</b>) small hole seeding accuracy, (<b>b</b>) large hole seeding accuracy, (<b>c</b>) small hole seed dispersion, (<b>d</b>) large hole seed dispersion.</p>
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<p>Regression analysis between vibration frequency of seed displacer and seeding accuracy and dispersion: (<b>a</b>) small hole seeding accuracy, (<b>b</b>) large hole seeding accuracy, (<b>c</b>) small hole seed dispersion, (<b>d</b>) large hole seed dispersion.</p>
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18 pages, 4420 KiB  
Article
Toxicity of Ethyl Formate to Tribolium castaneum (Herbst) Exhibiting Different Levels of Phosphine Resistance and Its Influence on Metabolite Profiles
by Changyao Shan, Xinyue You, Li Li, Xin Du, Yonglin Ren and Tao Liu
Agriculture 2024, 14(2), 323; https://doi.org/10.3390/agriculture14020323 - 18 Feb 2024
Cited by 1 | Viewed by 1200
Abstract
Ethyl formate (EF), a naturally occurring fumigant, has attracted widespread attention owing to its low toxicity in mammals. Here, Direct Immersion Solid-Phase Microextraction (DI-SPME) was employed for sample preparation in mass spectrometry-based untargeted metabolomics to evaluate the effects on Tribolium castaneum (Herbst) strains [...] Read more.
Ethyl formate (EF), a naturally occurring fumigant, has attracted widespread attention owing to its low toxicity in mammals. Here, Direct Immersion Solid-Phase Microextraction (DI-SPME) was employed for sample preparation in mass spectrometry-based untargeted metabolomics to evaluate the effects on Tribolium castaneum (Herbst) strains with different levels of PH3 resistance (sensitive, TC-S; moderately resistant, TC-M; strongly resistant, TC-SR) when exposed to a sub-lethal concentration (LC30) of EF. The bioassay indicated that T. castaneum strains with varying PH3 resistance levels did not confer cross-resistance to EF. A metabolomic analysis revealed that exposure to sublethal doses of EF significantly altered 23 metabolites in T. castaneum, including 2 that are unique to the species which remained unaffected by external conditions, while 11 compounds showed a strong response. A pathway topology analysis indicated that EF caused changes to several metabolic pathways, mainly involving fatty acids and their related metabolic pathways. This study showed that EF can induce highly similar metabolic responses in insects across varying levels of PH3 resistance, suggesting that the mechanisms driving the toxicity of EF and PH3 are distinct. These insights significantly extend our knowledge of the toxic mechanisms of EF and provide direct evidence for the efficacy of EF treatment for managing PH3 resistance in insects. Full article
(This article belongs to the Special Issue Postharvest Biosecurity of Agricultural Products)
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<p>Metabolites obtained in control and EF treatment groups of <span class="html-italic">Tribolium castaneum</span> (Herbst). The points highlighted in red are significant compounds selected based on the <span class="html-italic">p</span>-value threshold (&lt;0.05), and the green points represent nonsignificant compounds. Each point represents three biological replicates.</p>
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<p>Clustering heatmap analysis. The left heatmap (<b>a</b>) shows differentially abundant metabolite modules between ethyl formate treatment and the control group (<b>b</b>), and the right heatmap (<b>c</b>) shows the different phosphate-resistant levels in relation to the control group.</p>
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<p>The architecture of the Artificial Neural Network (ANN) model constructed from the DI-SPME-GCMS metabolite profiles of <span class="html-italic">Tribolium castaneum</span> (Herbst) with different levels of PH<sub>3</sub> resistance in both EF-treated and untreated groups. W and b are the network’s adjustable parameters, representing the weight matrices and bias vectors, respectively. Once the network is trained, its bias and weight values form into a vector. This single vector is then redivided into the original biases and weights.</p>
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<p>Confusion matrices displaying the overall accuracy and errors in classifications. The green squares along the diagonal of the matrix indicate correct classifications, while the red squares show where misclassifications have occurred. Each cell box provides the count and proportion of the <span class="html-italic">Tribolium castaneum</span> (Herbst) samples. A well-performing network is characterized by lower percentages in the red squares, signifying minimal misclassifications. Different sets of confusion matrices are presented as follows: (<b>a</b>) training set, (<b>b</b>) validation set, (<b>c</b>) testing set, and (<b>d</b>) all confusion matrices in one matrix.</p>
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<p>Principal Component Analysis (PCA) (<b>a</b>) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) (<b>b</b>) indicate a significant separation between treatment with ethyl formate and controls for metabolome. These changes are not associated with phosphine resistance levels. The reliability of the OPLS-DA model was determined using a permutational test.</p>
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<p>Variable importance projection (VIP) scores from OPLS-DA analysis of adult <span class="html-italic">Tribolium castaneum</span> (Herbst) samples showing eleven significantly different metabolites between the control and EF treatment groups (<b>a</b>). Volcano plot of significantly upregulated (red) and downregulated (blue) metabolites (<span class="html-italic">p</span> ≤ 0.05 and |Log<sub>2</sub>(FoldChange)| ≥ 0.5) (<b>b</b>).</p>
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<p>KEGG enrichment analysis of 11 differential metabolites (VIP ≥ 1, <span class="html-italic">p</span> ≤ 0.05, and Log<sub>2</sub>(FoldChange) ≥ 0.5) in <span class="html-italic">Tribolium castaneum</span> (Herbst) with ethyl formate treatment. The results were visualized using a bar plot and bubble diagram. Bar plot: Gradients of colors are based on the <span class="html-italic">p</span>-value (<b>a</b>). Bubble plot: Gradients of colors are based on the <span class="html-italic">p</span>-value. The size of the circle represents the hit compounds (<b>b</b>).</p>
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<p>Schematic network of the identified lipid metabolism pathways in the ethyl formate treatment group of <span class="html-italic">Tribolium castaneum</span> (Herbst).</p>
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23 pages, 3522 KiB  
Article
Cherry Tree Crown Extraction Using Machine Learning Based on Images from UAVs
by Vasileios Moysiadis, Ilias Siniosoglou, Georgios Kokkonis, Vasileios Argyriou, Thomas Lagkas, Sotirios K. Goudos and Panagiotis Sarigiannidis
Agriculture 2024, 14(2), 322; https://doi.org/10.3390/agriculture14020322 - 18 Feb 2024
Cited by 2 | Viewed by 1580
Abstract
Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially [...] Read more.
Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially for orchards, it is helpful to isolate each tree and then calculate the preferred vegetation indices separately. Thus, tree detection and crown extraction is another important research area in the domain of Smart Farming. In this paper, we propose an innovative tree detection method based on machine learning, designed to isolate each individual tree in an orchard. First, we evaluate the effectiveness of Detectron2 and YOLOv8 object detection algorithms in identifying individual trees and generating corresponding masks. Both algorithms yield satisfactory results in cherry tree detection, with the best F1-Score up to 94.85%. In the second stage, we apply a method based on OTSU thresholding to improve the provided masks and precisely cover the crowns of the detected trees. The proposed method achieves 85.30% on IoU while Detectron2 gives 79.83% and YOLOv8 has 75.36%. Our work uses cherry trees, but it is easy to apply to any other tree species. We believe that our approach will be a key factor in enabling health monitoring for each individual tree. Full article
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<p>Simplified diagrams of architecture for (<b>a</b>) Mask-RCNN architecture [<a href="#B35-agriculture-14-00322" class="html-bibr">35</a>], and (<b>b</b>) YOLO architecture.</p>
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<p>Methodology Diagram.</p>
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<p>Location of the experimental area (Grevena Prefecture, Greece).</p>
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<p>UAV and camera used to capture photos in the experimental area.</p>
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<p>Aspect ratios of anchor boxes.</p>
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<p>Orthomosaic image of an orchard with cherry trees: (<b>a</b>) in visible band (RGB), (<b>b</b>) in greyscale based on NDVI index.</p>
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<p>Example of OTSU thresholding for a cherry tree: (<b>a</b>) Grayscale image of the surrounding area of a specific tree on the NDVI, (<b>b</b>) Grayscale image of the tree on NDVI after gamma correction, (<b>c</b>) Black and white image after applying OTSU thresholding method.</p>
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<p>Histogram of the grayscaled image based on NDVI index for the surrounding area of a cherry tree.</p>
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<p>Mask of the orchard based on OTSU thresholding after gamma correction.</p>
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<p>Comparison of the masks provided for a specific tree. (<b>a</b>) Cherry tree from the orchard. (<b>b</b>) Ground truth mask. (<b>c</b>) Mask from Detectron2. (<b>d</b>) Mask from YOLOv8.</p>
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<p>Comparison of the masks provided for a specific tree. (<b>a</b>) Cherry tree from the orchard. (<b>b</b>) Ground truth mask. (<b>c</b>) Mask from Detectron2. (<b>d</b>) Mask from YOLOv8.</p>
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<p>Example of changes in the mask of a specific tree. (<b>a</b>) Modifications on the provided mask from YOLOv8. (<b>b</b>) Final mask after OTSU thresholding. (<b>c</b>) The perimeter of the mask on the orthomosaic image for the specific cherry tree.</p>
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<p>Convergence of Precision and Recall in Detectron2 with ResNet-101 and ResNet-50.</p>
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<p>Convergence of Precision and Recall in YOLOv8 with YOLOv8x-seg and YOLOv8m-seg.</p>
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<p>Examples of detected cherry trees in orchards: (<b>a</b>) Orchard that has mostly adult trees and moderate coverage in grass and weeds, (<b>b</b>) Orchard with young trees, (<b>c</b>) Orchard with high presence of weeds and grass.</p>
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<p>Detected masks from Detectron2: (<b>a</b>) Corresponding mask of a cherry tree. (<b>b</b>) All masks of the orchard combined in one image.</p>
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<p>Examples of improved masks based on the OTSU thresholding. Dark grey pixels indicate the removed area, while light grey pixels indicate the added area.</p>
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<p>NDVI for a specific tree in four different flights. (<b>a</b>) On 6 May 2019. (<b>b</b>) On 13 June 2019. (<b>c</b>) On 25 May 2020. (<b>d</b>) On 24 July 2020.</p>
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<p>Example of excluding the shadow of a cherry tree. (<b>a</b>) Part of the orthomosaic image; (<b>b</b>) Mask detected from Detectron2; (<b>c</b>) NDVI after gamma correction; (<b>d</b>) Final mask with no shadow.</p>
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<p>Resolving overlapped masks. (<b>a</b>) Original image. (<b>b</b>) Overlapped masks from Detectron2. (<b>c</b>) Separated masks from the proposed method.</p>
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<p>Samples from the dataset from three different orchards. Four orthomosaic images were captured for each orchard in two consecutive years.</p>
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<p>Samples from the dataset from three different orchards. Four orthomosaic images were captured for each orchard in two consecutive years.</p>
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15 pages, 4697 KiB  
Article
Study on the Impact of Cutting Platform Vibration on Stalk Cutting Quality in Industrial Hemp
by Kunpeng Tian, Jicheng Huang, Bin Zhang, Aimin Ji and Zhonghua Xu
Agriculture 2024, 14(2), 321; https://doi.org/10.3390/agriculture14020321 - 18 Feb 2024
Cited by 1 | Viewed by 837
Abstract
In response to the unclear impact of header vibration on cutting performance (stalk cutting quality and cutting energy consumption) during field operations of industrial hemp harvesters, this study utilized a vibration recorder to collect information on header vibration during the operation of industrial [...] Read more.
In response to the unclear impact of header vibration on cutting performance (stalk cutting quality and cutting energy consumption) during field operations of industrial hemp harvesters, this study utilized a vibration recorder to collect information on header vibration during the operation of industrial hemp harvesters. Through data processing, the dominant range for the resonant frequency and amplitude of the cutting platform is primarily concentrated between 5–45 Hz and 0–35 mm, respectively. Using numerical simulation techniques, a quadratic orthogonal rotation combination experiment was conducted with vibration frequency and amplitude as experimental factors and stalk cutting quality and cutting energy consumption as indicators. Regression equations were established to reveal the relationships between indicators and factors, elucidating the influence of each factor and its interactions on the indicators. Specifically, for the crack length indicator, the amplitude has a highly significant influence on the model, and there is a significant interaction effect between vibration frequency and amplitude on the model. As for the cutting energy consumption indicator, frequency and the interaction between frequency and amplitude significantly affect the model, while amplitude has an extremely significant impact on the model. Through comprehensive fuzzy evaluation, the optimal vibration parameter combination satisfying comprehensive cutting performance indicators was determined as a vibration frequency of 37.86 Hz and an amplitude of 5.34 mm. Furthermore, the reliability of the model has been further validated. This research can provide a reference for improving the field performance of industrial hemp harvesters. Full article
(This article belongs to the Special Issue Agricultural Machinery Design and Agricultural Engineering)
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<p>CEM DT-178A vibration recorder.</p>
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<p>Vibrational information collection.</p>
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<p>Original vibration information of the cutting platform.</p>
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<p>The frequency displacement spectrum chart.</p>
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<p>Schematic diagram of bionic blade structure. Notes: <span class="html-italic">a</span> represents blade width; <span class="html-italic">b</span> represents spacing between fixed holes; <span class="html-italic">c</span> represents blade length; <span class="html-italic">d</span> represents blade edge length; <span class="html-italic">e</span> represents spacing between fixed holes and the lower end of the blade; <span class="html-italic">f</span> represents blade tooth inclination angle; <span class="html-italic">g</span> represents blade tooth width; <span class="html-italic">h</span> represents fixed hole diameter; <span class="html-italic">t</span> represents blade thickness; <span class="html-italic">s</span> represents bionic tooth curve.</p>
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<p>The numerical simulation results for stalk stubble and cracks.</p>
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<p>Stalk cutting process. Notes: <span class="html-italic">v<sub>UH</sub></span> represents horizontal motion of the upper blade; <span class="html-italic">v<sub>LH</sub></span> represents horizontal motion of the lower blade; <span class="html-italic">v<sub>UV</sub></span> represents vertical motion of the upper blade; <span class="html-italic">v<sub>LV</sub></span> represents vertical motion of the lower blade.</p>
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<p>Graph of the variation in cutting reaction forces for upper and lower blades.</p>
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<p>The influence of interaction effects on crack length.</p>
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<p>The influence of interaction effects on cutting energy.</p>
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17 pages, 3150 KiB  
Article
Regional Perspective of Using Cyber Insurance as a Tool for Protection of Agriculture 4.0
by Maksym W. Sitnicki, Nataliia Prykaziuk, Humeniuk Ludmila, Olena Pimenowa, Florin Imbrea, Laura Șmuleac and Raul Pașcalău
Agriculture 2024, 14(2), 320; https://doi.org/10.3390/agriculture14020320 - 18 Feb 2024
Viewed by 1383
Abstract
The digitalization of the agricultural industry is manifested through the active use of innovative technologies in all its areas. Agribusiness owners have to constantly improve their security to meet new challenges. In this context, the existing cyber risks of the agrarian industry were [...] Read more.
The digitalization of the agricultural industry is manifested through the active use of innovative technologies in all its areas. Agribusiness owners have to constantly improve their security to meet new challenges. In this context, the existing cyber risks of the agrarian industry were assessed and their classification by possible consequences, such as data theft or alteration, cyber terrorism, cyber warfare, software hacking or modification, the blocking of markets and transactions on them, was proposed. Cyber insurance is an effective tool for minimizing the likelihood of cyber incidents and for comprehensive post-incident support, with the involvement of cybersecurity specialists. An algorithm for cooperation between an agricultural company and an insurance company when concluding a cyber risk insurance contract is proposed, which takes into account the needs and wishes of insurers at each stage of the interaction. To assess the need to use cyber insurance in agriculture 4.0, a methodology has been developed to evaluate the regional characteristics of cybersecurity and the digitalization of agribusiness. The results of the study show a heterogeneous need for this tool in different regions of the world. Full article
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<p>Research framework for the necessity of using cyber insurance in agricultural industry 4.0.</p>
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<p>Cyber risk groups for key innovative tools of agricultural sector.</p>
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<p>Regional split of agricultural IoT market in 2022. <span class="html-italic">Source: Calculated by authors based on the Precedence Statistics of the Internet of Things (IoT) in Agriculture Market</span>.</p>
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<p>Regional split of agricultural robotics market 2018–2023. <span class="html-italic">Source: Calculated by authors based on the Researchdive Statistics of the Agriculture Robot Market</span>.</p>
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<p>Algorithm of cooperation between an agricultural company and an insurance company when concluding a cyber risk insurance contract.</p>
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<p>Map of the necessity of cyber insurance for agricultural industry based on 2022 numbers.</p>
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21 pages, 10047 KiB  
Article
Optimal Design and Dynamic Characteristic Analysis of Double-Link Trapezoidal Suspension for 3WPYZ High Gap Self-Propelled Sprayer
by Changxi Liu, Jun Hu, Zhaonan Yu, Yufei Li, Shengxue Zhao, Qingda Li and Wei Zhang
Agriculture 2024, 14(2), 319; https://doi.org/10.3390/agriculture14020319 - 17 Feb 2024
Viewed by 995
Abstract
A fast spraying speed, wide working area, and easy operation are the operational advantages of high-clearance boom sprayers. To address the issue of spray boom mechanical vibration affecting the spraying effect, a double-link trapezoidal boom suspension is designed for the 3WPYZ sprayer. This [...] Read more.
A fast spraying speed, wide working area, and easy operation are the operational advantages of high-clearance boom sprayers. To address the issue of spray boom mechanical vibration affecting the spraying effect, a double-link trapezoidal boom suspension is designed for the 3WPYZ sprayer. This suspension can achieve passive vibration reduction, active balance, and ground profiling. The kinematic model of the boom suspension is established based on D’Alembert’s principle and the principle of multi-body dynamics, and the design factors affecting the stability of the boom are determined. Through orthogonal experimental design and virtual kinematics simulation, the influence of the boom length and orifice diameter of each part on the swing angle and the natural frequency of the boom suspension is investigated. Design-Expert 8.0.6 software is used to analyze and optimize the test results. The optimization results show that, when the connecting boom length LAB is 265 mm, the inner boom suspension boom length LAD is 840 mm, the outer boom suspension boom length LBC is 1250 mm, and the throttle hole diameter d is 4 mm; the maximum swing angle of the boom suspension is reduced by 53.02%. In addition, the natural frequency of the boom is reduced from 1.3143 rad/s to 1.1826 rad/s, and the dynamic characteristic optimization effect is remarkable. The modal analysis results show that the first sixth-order vibration test frequency of the boom sprayer suspension designed in this paper meets the requirements and avoids the influence of external factors. Field tests show that, when the sprayer is excited by the environment at 3.5° to 4°, the boom suspension can reduce the vibration transmitted by the body to a reasonable range. The static analysis shows that the structural design of this study reduces the stress at the connection of the end boom suspension, the maximum displacement, and the maximum stress of the inner boom suspension. The test results of the dynamic characteristics of the implement are basically consistent with the virtual model simulation test results, thus achieving the optimization objectives. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Schematic diagram of the working principle of the boom suspension.</p>
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<p>Force analysis diagram of boom suspension.</p>
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<p>A 3D model of the two-link trapezoidal boom suspension. (<b>a</b>) Middle boom suspension; (<b>b</b>) single-sided boom suspension.</p>
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<p>The virtual prototype of boom suspension based on ADAMS.</p>
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<p>Construction and geometrical dimensions of specimens.</p>
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<p>The dynamic characteristics test of 3WPYZ high gap self-propelled sprayer boom suspension.</p>
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<p>Time domain response diagram of boom suspension. (<b>a</b>) The excitation amplitude is 5°; (<b>b</b>) the excitation amplitude is 25°.</p>
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<p>Dynamic characteristic diagram of boom suspension under different excitation frequencies. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ω</mi> </mrow> </semantics></math> = 0.1 rad/s; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ω</mi> </mrow> </semantics></math> = 0.5 rad/s; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ω</mi> </mrow> </semantics></math> = 5 rad/s; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ω</mi> </mrow> </semantics></math> = 10 rad/s.</p>
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<p>The relationship curve between the maximum swing angle of the external spray boom suspension and the frequency under the change in <span class="html-italic">L<sub>AD</sub></span>.</p>
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<p>Relationship between boom suspension swing angle and <span class="html-italic">L<sub>AD</sub></span>. (<b>a</b>) The swing angle at <span class="html-italic">w</span> = 10 rad/s; (<b>b</b>) relation at natural frequencies.</p>
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<p>Relationship between boom suspension swing angle and <span class="html-italic">L<sub>BC</sub></span>. (<b>a</b>) The swing angle at <span class="html-italic">w</span> = 10 rad/s; (<b>b</b>) relation at natural frequencies.</p>
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<p>Relationship between boom suspension swing angle and <span class="html-italic">L<sub>AB</sub></span>. (<b>a</b>) The swing angle at <span class="html-italic">w</span> = 10 rad/s; (<b>b</b>) relation at natural frequencies.</p>
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<p>Relationship between boom suspension swing angle and <span class="html-italic">d</span>. (<b>a</b>) The swing angle at <span class="html-italic">w</span> = 10 rad/s; (<b>b</b>) relation at natural frequencies.</p>
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<p>Diagram of the maximum swing angle and frequency relationship between the before and after of the boom suspension improvement. (<b>a</b>) Before improvement; (<b>b</b>) after improvement.</p>
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<p>Displacement and stress cloud diagram boom suspension. (<b>a</b>) Displacement cloud of single-side boom; (<b>b</b>) stress cloud of single-side boom; (<b>c</b>) displacement cloud of single-side boom; and (<b>d</b>) stress cloud diagram of external boom.</p>
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<p>The first six non-rigid modes of the boom suspension. (<b>a</b>) First mode shape; (<b>b</b>) second mode shape; (<b>c</b>) third mode shape; (<b>d</b>) fourth mode shape; (<b>e</b>) fifth mode shape; and (<b>f</b>) sixth mode shape.</p>
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<p>Boom suspension swing test results. (<b>a</b>) Height is 500 mm; (<b>b</b>) height is 1200 mm; and (<b>c</b>) height is 2500 mm.</p>
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21 pages, 7447 KiB  
Article
DiffuCNN: Tobacco Disease Identification and Grading Model in Low-Resolution Complex Agricultural Scenes
by Huizhong Xiong, Xiaotong Gao, Ningyi Zhang, Haoxiong He, Weidong Tang, Yingqiu Yang, Yuqian Chen, Yang Jiao, Yihong Song and Shuo Yan
Agriculture 2024, 14(2), 318; https://doi.org/10.3390/agriculture14020318 - 17 Feb 2024
Viewed by 1328
Abstract
A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model significantly enhances the accuracy of detecting tobacco lesions under low-resolution [...] Read more.
A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model significantly enhances the accuracy of detecting tobacco lesions under low-resolution conditions. After detecting lesions, the grading of the disease severity is achieved through counting. The key features of DiffuCNN include a resolution enhancement module based on diffusion, an object detection network optimized through filter pruning, and the employment of the CentralSGD optimization algorithm. Experimental results demonstrate that DiffuCNN surpasses other models in precision, with respective values of 0.98 on precision, 0.96 on recall, 0.97 on accuracy, and 62 FPS. Particularly in counting tobacco lesions, DiffuCNN exhibits an exceptional performance, attributable to its efficient network architecture and advanced image processing techniques. The resolution enhancement module based on diffusion amplifies minute details and features in images, enabling the model to more effectively recognize and count tobacco lesions. Concurrently, filter pruning technology reduces the model’s parameter count and computational burden, enhancing the processing speed while retaining the capability to recognize key features. The application of the CentralSGD optimization algorithm further improves the model’s training efficiency and final performance. Moreover, an ablation study meticulously analyzes the contribution of each component within DiffuCNN. The results reveal that each component plays a crucial role in enhancing the model performance. The inclusion of the diffusion module significantly boosts the model’s precision and recall, highlighting the importance of optimizing at the model’s input end. The use of filter pruning and the CentralSGD optimization algorithm effectively elevates the model’s computational efficiency and detection accuracy. Full article
(This article belongs to the Special Issue Advanced Image Processing in Agricultural Applications)
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<p>Diverse tobacco leaf dataset. The collection showcases a variety of leaf conditions and environments: from left to right, leaves with powdery mildew, leaves in natural outdoor settings with potential pest damage, healthy leaves with complex backgrounds, and leaves exhibiting symptoms of potential disease or stress factors. This dataset highlights the variability in lighting, leaf orientation, and background complexity, which poses challenges for accurate disease detection and counting.</p>
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<p>Image dataset annotation screenshot.</p>
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<p>Dataset augmentation: (<b>A</b>,<b>B</b>) are cutout (the black and white square are the random removal); (<b>C</b>,<b>D</b>) are image synthesis.</p>
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<p>Schematic representation of the diffusion-based resolution enhancement module used in the DiffuCNN model. The process involves (<b>a</b>) estimating the degradation model through a series of transformations from an initial noisy image <math display="inline"><semantics> <msub> <mi>x</mi> <mi>t</mi> </msub> </semantics></math> to a guidance image <span class="html-italic">y</span>, (<b>b</b>) GDP-<math display="inline"><semantics> <msub> <mi>x</mi> <mi>t</mi> </msub> </semantics></math> showing the denoising process with multiple degradation models leading to the final restored image, and (<b>c</b>) GDP-<math display="inline"><semantics> <msub> <mi>x</mi> <mn>0</mn> </msub> </semantics></math> where <math display="inline"><semantics> <msub> <mi>x</mi> <mn>0</mn> </msub> </semantics></math> estimation is refined iteratively. Each step is supervised to ensure fidelity to the target image, with the overall aim to guide the restoration process and enhance image resolution for improved disease detection in agricultural imagery.</p>
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<p>Diagram illustrating the network structure design for filter pruning in the DiffuCNN model. On the left, the process starts with an initial convolutional layer (conv1) followed by a subsequent layer (conv2), where ineffective filters are identified and removed, as indicated by the red cross. The right part of the figure emphasizes the refined pruning process where convolutional filters are selectively pruned based on their contribution to the output (highlighted by the red stripes), and the green check marks indicate the retention of significant filters that are added to the subsequent layers.</p>
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<p>Visualization of lesion detection results. (<b>A</b>) is DiffuCNN; (<b>B</b>) is YOLOv8; (<b>C</b>) is MAF50; (<b>D</b>) is RetinaNet; (<b>E</b>) is CenterNet; (<b>F</b>) is SSD; (<b>G</b>) is Faster R-CNN. The red box is the prediction bounding boxes given by these methods.</p>
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<p>Our model training processing.</p>
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17 pages, 3216 KiB  
Article
Influence of Swine Wastewater Irrigation and Straw Return on the Accumulation of Selected Metallic Elements in Soil and Plants
by Siyi Li, Zhen Tao, Yuan Liu, Shengshu Li, Rakhwe Kama, Chao Hu, Xiangyang Fan and Zhongyang Li
Agriculture 2024, 14(2), 317; https://doi.org/10.3390/agriculture14020317 - 17 Feb 2024
Cited by 2 | Viewed by 1156
Abstract
Treated livestock wastewater reuse for irrigation and straw return in arid regions have become common practices worldwide. However, many uncertainties still exist regarding the effects of the returning straw sizes on heavy metal accumulation in soil and plants under treated livestock wastewater irrigation. [...] Read more.
Treated livestock wastewater reuse for irrigation and straw return in arid regions have become common practices worldwide. However, many uncertainties still exist regarding the effects of the returning straw sizes on heavy metal accumulation in soil and plants under treated livestock wastewater irrigation. In a pot experiment growing maize and soybean, large (5–10 cm), medium (1–5 cm), and small (<1 cm) sizes of wheat straw were amended to assess the changes in Cu and Zn distribution in the rhizosphere, bulk soils, and plants. Groundwater and swine wastewater were used as irrigation water resources. The results showed that irrigation with swine wastewater significantly reduced soil pH and increased the concentration of soil-available potassium. Concentrations of Cu in soil were more sensitive to swine wastewater and straw application than those of Zn in soil. Swine wastewater irrigation increased the accumulation of Cu and Zn in plants with higher concentrations of Zn, while straw return tended to inhibit this increase, especially when a small size of straw was employed. In addition to providing a reference for revealing the interaction mechanism between swine wastewater irrigation and straw return, this study proposes feasible solutions to improve the efficiency of agricultural waste recycling and realize sustainable agricultural development. Full article
(This article belongs to the Section Crop Production)
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<p>Soil pH (<b>a</b>), organic matter (<b>b</b>), available potassium (<b>c</b>), available phosphorus (<b>d</b>), nitrate-nitrogen (<b>e</b>), and ammonium-nitrogen (<b>f</b>) of the rhizosphere and bulk soil of maize under the treatments of different irrigation water resources and straw sizes. GW refers to groundwater, and SW refers to swine wastewater. Four sizes of straw (CK, 0–1 cm, 1–5 cm, and 5–10 cm) were applied to the soil. Different lowercase letters on the right of the light-blue and dark-blue columns represent significant differences in soil properties among the different treatments of rhizosphere and bulk soils, respectively at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Soil pH (<b>a</b>), organic matter (<b>b</b>), available potassium (<b>c</b>), available phosphorus (<b>d</b>), nitrate-nitrogen (<b>e</b>), and ammonium-nitrogen (<b>f</b>) of the rhizosphere and bulk soil of soybeans under the treatments of different irrigation water resources and straw sizes. GW refers to groundwater, and SW refers to swine wastewater. Four sizes of straw (CK, 0–1 cm, 1–5 cm, and 5–10 cm) were applied to the soil. Different lowercase letters on the right of the light-green and dark-green columns represent significant differences in soil properties among different treatments of the rhizosphere and bulk soils, respectively, at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Redundancy analysis presenting the association between rhizospheric properties and Cu/Zn content in the soils and fruits of maize (<b>a</b>) and soybeans (<b>b</b>). “CK”, “0–1 cm”, “1–5 cm”, and “5–10 cm” represent the four straw return treatments with different sizes. Cu(R) refers to the Cu content in the rhizosphere, Cu(F) refers to Cu the content in the maize or soybean fruits, Zn(R) refers to the Zn content in the rhizosphere, Zn(F) refers to the Zn content in the maize or soybean fruits, OM refers to organic matter, AP refers to available phosphorus, AK refers to available potassium, Nitrate refers to nitrate-nitrogen, and Ammonium refers to ammonium-nitrogen.</p>
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<p>Cu concentrations in the soil of maize (<b>a</b>) and soybeans (<b>b</b>) and Zn concentrations in the soil of maize (<b>c</b>) and soybeans (<b>d</b>). “CK”, “0–1 cm”, “1–5 cm”, and “5–10 cm” represent the four straw return treatments with different sizes. GW refers to groundwater, and SW refers to swine wastewater. Different lowercase letters on the center of the light-color and dark-color columns represent significant differences in soil properties among different treatments of the rhizosphere and bulk soils, respectively, at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Cu concentrations in the plant tissues of maize (<b>a</b>) and soybeans (<b>b</b>) and the Zn concentrations in the plant tissues of maize (<b>c</b>) and soybeans (<b>d</b>). “CK”, “0–1 cm”, “1–5 cm”, and “5–10 cm” represent the four straw return treatments with different sizes. GW refers to groundwater, and SW refers to swine wastewater. Different lowercase letters on the same color columns represent significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Bioaccumulation factor (BF) of heavy metals for maize (<b>a</b>) and soybeans (<b>b</b>). “CK”, “0–1 cm”, “1–5 cm”, and “5–10 cm” represent the four straw return treatments with different sizes. GW refers to groundwater, and SW refers to swine wastewater. Different lowercase letters on the center of the light-color and dark-color columns represent significant differences in the BFs of Cu and Zn among different treatments, respectively, at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Translocation factor (TF) of heavy metals for maize (<b>a</b>) and soybeans (<b>b</b>). “CK”, “0–1 cm”, “1–5 cm”, and “5–10 cm” represent the four straw return treatments with different sizes. GW refers to groundwater, and SW refers to swine wastewater. Different lowercase letters on the center of the light-color and dark-color columns represent significant differences in the TF of Cu and Zn among different treatments, respectively, at <span class="html-italic">p</span> &lt; 0.05.</p>
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14 pages, 4321 KiB  
Article
Indoor Temperature Forecasting in Livestock Buildings: A Data-Driven Approach
by Carlos Alejandro Perez Garcia, Marco Bovo, Daniele Torreggiani, Patrizia Tassinari and Stefano Benni
Agriculture 2024, 14(2), 316; https://doi.org/10.3390/agriculture14020316 - 17 Feb 2024
Viewed by 1197
Abstract
The escalating global population and climate change necessitate sustainable livestock production methods to meet rising food demand. Precision Livestock Farming (PLF) integrates information and communication technologies (ICT) to improve farming efficiency and animal health. Unlike traditional methods, PLF uses machine learning (ML) algorithms [...] Read more.
The escalating global population and climate change necessitate sustainable livestock production methods to meet rising food demand. Precision Livestock Farming (PLF) integrates information and communication technologies (ICT) to improve farming efficiency and animal health. Unlike traditional methods, PLF uses machine learning (ML) algorithms to analyze data in real time, providing valuable insights to decision makers. Dairy farming in diverse climates is challenging and requires well-designed structures to regulate internal environmental parameters. This study explores the application of the Facebook-developed Prophet algorithm to predict indoor temperatures in a dairy farm over a 72 h horizon. Exogenous variables sourced from the Open-Meteo platform improve the accuracy of the model. The paper details case study construction, data acquisition, preprocessing, and model training, highlighting the importance of seasonality in environmental variables. Model validation using key metrics shows consistent accuracy across different dates, as the mean absolute percentage error on daily base ranges from 1.71% to 2.62%. The results indicate excellent model performance, especially considering the operational context. The study concludes that black box models, such as the Prophet algorithm, are effective for predicting indoor temperatures in livestock buildings and provide valuable insights for environmental control and optimization in livestock production. Future research should explore gray box models that integrate physical building characteristics to improve predictive performance and HVAC system control. Full article
(This article belongs to the Special Issue Optimization of Livestock Housing Management)
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<p>Three-dimensional view of the case study farm.</p>
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<p>Plan view of the sensor position.</p>
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<p>Data cleaning workflow.</p>
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<p>Local weather station vs. Open-Meteo data.</p>
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<p>Linear correlation between the local weather station and Open-Meteo data.</p>
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<p>Model description and data structure.</p>
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<p>Model input variables.</p>
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<p>Indoor temperature predictions from the model.</p>
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<p>Residuals analysis for rolling windows prediction.</p>
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16 pages, 4210 KiB  
Article
Enhancing Safety through Optimal Placement of Components in Hydrogen Tractor: Rollover Angle Analysis
by Jinho Son, Yeongsu Kim, Seokho Kang and Yushin Ha
Agriculture 2024, 14(2), 315; https://doi.org/10.3390/agriculture14020315 - 16 Feb 2024
Cited by 3 | Viewed by 1076
Abstract
Hydrogen tractors are being developed, necessitating consideration of the variation in the center of gravity depending on the arrangement of components such as power packs and cooling modules that replace traditional engines. This study analyzes the effects of component arrangement on stability and [...] Read more.
Hydrogen tractors are being developed, necessitating consideration of the variation in the center of gravity depending on the arrangement of components such as power packs and cooling modules that replace traditional engines. This study analyzes the effects of component arrangement on stability and rollover angle in hydrogen tractors through simulations and proposes an optimal configuration. Stability is evaluated by analyzing rollover angles in various directions with rotations around the tractor’s midpoint. Based on the analysis of rollover angles for Type 1, Type 2, and Type 3 hydrogen tractors, Type 2 demonstrates superior stability compared to the other types. Specifically, when comparing lateral rollover angles at 0° rotation, Type 2 exhibits a 2% increase over Type 3. Upon rotations at 90° and 180°, Type 2 consistently displays the highest rollover angles, with differences ranging from approximately 6% to 12% compared to the other types. These results indicate that Type 2, with its specific component arrangement, offers the most stable configuration among the three types of tractors. It is confirmed that the rollover angle changes based on component arrangement, with a lower center of gravity resulting in greater stability. These findings serve as a crucial foundation for enhancing stability in the future design and manufacturing phases of hydrogen tractors. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Layout of the main components in the combustion engine and hydrogen tractor: (<b>a</b>) Combustion engine tractor; (<b>b</b>) Type 1 hydrogen tractor; (<b>c</b>) Type 2 hydrogen tractor; and (<b>d</b>) Type 3 hydrogen tractor.</p>
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<p>Displacement of the main components of the combustion engine and hydrogen tractor in the simulation: (<b>a</b>) Combustion engine tractor; (<b>b</b>) Type 1 hydrogen tractor; (<b>c</b>) Type 2 hydrogen tractor; (<b>d</b>) Type 3 hydrogen tractor.</p>
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<p>Schematic diagram of rollover of tractor: (<b>a</b>) Normal condition; (<b>b</b>) Tilting condition.</p>
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<p>Diagram showing new coordinate system for different angles from xyz to XYZ.</p>
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<p>Schematic view of simulation method: (<b>a</b>) Critical condition (<b>b</b>) Rotate method.</p>
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<p>Flow chart of simulation methodology and assumptions considered in this study.</p>
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<p>Contact force as a function of the rollover angle of the tractor.</p>
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<p>Comparison of the rollover angles of the combustion engine tractor and the standard (Blue letter: rollover angle, Black letter: rotation angle).</p>
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<p>Comparison of the rollover angles of each type of tractor and the standard (Blue letter: rollover angle, Black letter: rotation angle).</p>
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<p>The LTR analysis for rotation angle of hydrogen tractor: (<b>a</b>) 0°, (<b>b</b>) 90°, (<b>c</b>) 180°, (<b>d</b>) 270°.</p>
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<p>The LTR analysis for rotation angle of hydrogen tractor: (<b>a</b>) 0°, (<b>b</b>) 90°, (<b>c</b>) 180°, (<b>d</b>) 270°.</p>
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19 pages, 5477 KiB  
Article
Design and Experimental Study of a Biomimetic Pod-Pepper-Picking Drum Based on Multi-Finger Collaboration
by Chuanxing Du, Weiquan Fang, Dianlei Han, Xuegeng Chen and Xinzhong Wang
Agriculture 2024, 14(2), 314; https://doi.org/10.3390/agriculture14020314 - 16 Feb 2024
Cited by 4 | Viewed by 1022
Abstract
In order to reduce ground drop loss during mechanical pepper picking and improve the net recovery rate, a drum snap finger picking device was designed. The picking device is mainly composed of a picking drum and auxiliary picking components; the picking finger arrangement [...] Read more.
In order to reduce ground drop loss during mechanical pepper picking and improve the net recovery rate, a drum snap finger picking device was designed. The picking device is mainly composed of a picking drum and auxiliary picking components; the picking finger arrangement was designed biomimetically and its structure and operating parameters were optimized by the DEM (discrete element method). According to the physical and mechanical characteristics of the pepper and the simplified three-dimensional model of the picking device, a virtual simulation model of the pepper-picking device was established using the EDEM software. Through simulation analysis and using the orthogonal test method, the main factors which affect the ground drop loss rate of pepper and their optimal parameter combination values were determined. The simulation results were verified by a pepper-picking field experiment. Orthogonal tests show that, when the picking drum speed (V) is 210 rpm, the pepper-feeding speed (V) is 1100 mm·s1, the bending angle of each picking spring tooth (C) is 162°, and each group of circumferential fingers has rows, the picking device has a good picking effect. At this time, the ground drop loss rates in both the simulation and field test were 7.50% and 7.85%, respectively, and the drop error was only 4.46%, which was within the allowable range. The design form and parameter optimization simulation method in this paper provide an important reference for the design and optimization of pepper-harvesting machinery. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Overall structure diagram of the pepper-picking device. 1. Ground-profiling wheel. 2. Picking drum. 3. Pepper-pressing wheel. 4. Pepper splitter. 5. Roller-upper baffle. 6. Picking rack. 7. Diagonal conveyor belt. 8. Baffle. 9. Transmission system. 10. Elevator conveyor belt. 11. Lifting cylinder.</p>
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<p>Schematic diagram of the main structure and parameters of the picking device.</p>
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<p>Pepper-picking posture of multi-finger synergistic joint movement of human hands.</p>
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<p>Schematic diagram of multi-finger collaborative arrangement scheme for picking fingers.</p>
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<p>Schematic diagram of picking finger arrangement at different times for each group of circumferential finger rows.</p>
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<p>Schematic diagram of influencing factors of ground drop loss rate of pod pepper. (<b>a</b>) Schematic diagram of the working parameters of the picking device; (<b>b</b>) arrangement scheme of bionic snap fingers; (<b>c</b>) structural diagram of picking snap fingers. <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>V</mi> </mrow> <mrow> <mo>′</mo> </mrow> </msup> </mrow> </semantics></math> is picking drum rotation speed; <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>V</mi> </mrow> <mrow> <mo>″</mo> </mrow> </msup> </mrow> </semantics></math> is pod pepper feeding speed; N is number of rows of fingers in each group; C is bending angle of picking fingers.</p>
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<p>Pepper discrete element parameters and model validation. (<b>a</b>) Before drawing the board; (<b>b</b>) after pumping; (<b>c</b>) pepper discrete element model; (<b>d</b>) pulling plate test.</p>
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<p>Two-dimensional and three-dimensional model of pod-pepper-picking platform. (<b>a</b>) Two-dimensional model; (<b>b</b>) three-dimensional model.</p>
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<p>Schematic diagram of the simulation process of pod-pepper-picking bench.</p>
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<p>Weight statistics of pod peppers during simulation process.</p>
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<p>Analysis of optimization results of Box–Behnken experiment.</p>
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<p>Field test and pod-pepper-picking device.</p>
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18 pages, 3422 KiB  
Article
Effects of Soluble Organic Fertilizer Combined with Inorganic Fertilizer on Greenhouse Tomatoes with Different Irrigation Techniques
by Binnan Li and Lixia Shen
Agriculture 2024, 14(2), 313; https://doi.org/10.3390/agriculture14020313 - 16 Feb 2024
Viewed by 1391
Abstract
A reasonable fertilization rate and appropriate irrigation technology can lead to the green development of greenhouse tomatoes. The purpose of this study was to explore suitable irrigation technology for greenhouse tomatoes and the appropriate application rate of the soluble organic fertilizer and inorganic [...] Read more.
A reasonable fertilization rate and appropriate irrigation technology can lead to the green development of greenhouse tomatoes. The purpose of this study was to explore suitable irrigation technology for greenhouse tomatoes and the appropriate application rate of the soluble organic fertilizer and inorganic fertilizer combination. In 2021 and 2022, the effects of different irrigation techniques and fertilization treatments on tomato plant growth, fruit quality, yield, and efficiency were studied. The irrigation techniques in this study were drip and Moistube irrigation, and there were seven types of fertilization treatment, namely, no fertilization treatment (CK); low-volume (T1, 330 kg/hm2), medium-volume (T2, 660 kg/hm2), and high-volume inorganic fertilizer treatments (T3, 990 kg/hm2); and three inorganic fertilizer treatments of low-volume inorganic fertilizer (T1, 330 kg/hm2) combined with low-volume (F1, T1 + 75 kg/hm2), medium-volume (F2, T1 + 225 kg/hm2), and high-volume (F3, T1 + 375 kg/hm2) organic fertilizer. A total of 14 experimental treatments were implemented for irrigation and fertilization. The results of the two-year experiment show that the growth effect on the height, stem diameter, and leaf area index of tomato plants was the best using the treatment of low-concentration inorganic fertilizer combined with medium-concentration organic fertilizer with Moistube irrigation and drip irrigation. Using the two irrigation methods, the application of soluble organic fertilizer increased the yield and improved the fruit quality of the tomato. The maximum yield increased by 28.52%, the soluble sugar content increased by 14.49%, the vitamin C content increased by 45.04%, and the lycopene increased by 18.79%. The entropy-weight TOPSIS model was used to comprehensively evaluate 14 evaluation objects with different irrigation methods and fertilization treatments. The results of the two-year experiment show that the best fertilization treatment under Moistube irrigation and drip irrigation conditions was low-concentration inorganic fertilizer combined with medium-concentration soluble organic fertilizer, which was combined with the best fertilization treatment, and the most suitable irrigation method for greenhouse tomato cultivation in the Loess Plateau was Moistube irrigation. The results of this study also provide practical experience and theoretical support for adaptive irrigation and the integrated management of water and fertilizer. Full article
(This article belongs to the Section Agricultural Water Management)
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<p>Greenhouse meteorological data for 2021 and 2022.</p>
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<p>Tomato plant growth for different irrigation and fertilization treatments. Note: (<b>a</b>) is plant height of moistube irrigation in 2021 and 2022, (<b>b</b>) is plant height of drip irrigation in 2021 and 2022, (<b>c</b>) is stem diameter of moistube irrigation in 2021 and 2022, (<b>d</b>) is stem diameter of drip irrigation in 2021 and 2022, (<b>e</b>) is leaf area index of moistube irrigation in 2021 and 2022, (<b>f</b>) is leaf area index of drip irrigation in 2021 and 2022, Different lowercase letters in the figure indicate the significance of the differences between the treatments. <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Tomato quality of different fertilization treatments under Moistube irrigation conditions. Note: (<b>a</b>) is soluble sugars, (<b>b</b>) is titratable acids, (<b>c</b>) is vitamin C, (<b>d</b>) is sugar-to-acid ratio, (<b>e</b>) is lycopene content, (<b>f</b>) is nitrate content. Different lowercase letters in the figure indicate the significance of the differences between the treatments. <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Tomato quality of different fertilization treatments under drip irrigation conditions. Note: (<b>a</b>) is soluble sugars, (<b>b</b>) is titratable acids, (<b>c</b>) is vitamin C, (<b>d</b>) is sugar-to-acid ratio, (<b>e</b>) is lycopene content, (<b>f</b>) is nitrate content. Different lowercase letters in the figure indicate the significance of the differences between the treatments. <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Tomato yield and fruit weight for different irrigation and fertilization treatments. Note: (<b>a</b>) is yield of moistube irrigation in 2021 and 2022, (<b>b</b>) is yield of drip irrigation in 2021 and 2022, (<b>c</b>) is single fruit weight of moistube irrigation in 2021 and 2022, (<b>d</b>) is single fruit weight of drip irrigation in 2021 and 2022. Different lowercase letters in the figure indicate the significance of the differences between the treatments. <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Partial fertilizer productivity and water use efficiency for different irrigation and fertilization treatments. Note: PFP is partial fertilizer productivity, WUE is water use efficiency. (<b>a</b>) is PFP of moistube irrigation in 2021 and 2022, (<b>b</b>) is PFP of drip irrigation in 2021 and 2022, (<b>c</b>) is WUE of moistube irrigation in 2021 and 2022, (<b>d</b>) is WUE of drip irrigation in 2021 and 2022. Different lowercase letters in the figure indicate the significance of the differences between the treatments. <span class="html-italic">p</span> &lt; 0.05.</p>
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12 pages, 6279 KiB  
Article
Enhancing the Performance of Sunflower Threshing Machines through Innovative Enhancements
by Khaled Abdeen Mousa Ali, Changyou Li, Han Wang, Ahmad Mostafa Mousa and Marwa Abd-Elnaby Mohammed
Agriculture 2024, 14(2), 312; https://doi.org/10.3390/agriculture14020312 - 16 Feb 2024
Cited by 1 | Viewed by 1411
Abstract
Improving the performance of the threshing process is of utmost importance in enhancing the quality of sunflower seeds and minimizing power consumption in sunflower production. In this study, we developed a modified sunflower threshing machine by incorporating two types of threshing rotors, namely [...] Read more.
Improving the performance of the threshing process is of utmost importance in enhancing the quality of sunflower seeds and minimizing power consumption in sunflower production. In this study, we developed a modified sunflower threshing machine by incorporating two types of threshing rotors, namely the angled rasp bar rotor and the tine bar rotor, as compared to the round bar rotor. The performance of these rotors was evaluated under various rotational speeds (150, 200, 250, and 300 rpm) and concave clearances (10, 15, and 20 mm). The evaluation parameters included machine throughput, the specific energy of threshing, the percentage of damaged seeds, the percentage of unthreshed seeds, and threshing efficiency. The results indicate that the specific energy decreased with an increase in rotor speed and a decrease in concave clearance, with the tine bar rotor exhibiting the lowest values. Threshing efficiency showed an increasing trend with higher rotor speeds and reduced concave clearance. The modifications made to the rotor design resulted in an enhanced threshing efficiency, with an improvement from 96.30% to 97.93% achieved at a rotor revolving speed of 300 rpm and a concave clearance of 10 mm. Moreover, the specific energy consumption reduced from 9.65 kW·h/ton to 5.09 kW·h/ton under the same operational conditions. These findings highlight the efficacy of the novel rotor design modifications in optimizing the performance of the stationary sunflower threshing machine, leading to improved efficiency and reduced energy consumption in sunflower seed threshing operations. Given its performance characteristics, this machine exhibits potential suitability for sunflower farms of small to medium scale. Full article
(This article belongs to the Section Agricultural Technology)
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<p>A 3D model for the stationary sunflower threshing machine. (1) Feeder; (2) power source; (3) power transmission; (4) separating unit; (5) seed’s outlet; (6) straw outlet; (7) thresher cover.</p>
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<p>Schematics (right) and photos (left) of the round bar (top), angled rasp bar (middle), and tine bar (bottom) rotors.</p>
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<p>Machine throughput (kg/h) vs. different clearances (mm) and different speeds (rpm).</p>
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<p>Effect of rotor speed (rpm) and rotor type on machine throughput (kg/h) at different concave clearances (mm). The data shown are averages of three replicates ± SD. Asterisks indicate significant differences from the round bar rotor at 5%.</p>
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<p>Consumed specific energy (kW.h/ton) vs. different clearances (mm) and different speeds (rpm).</p>
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<p>Effect of rotor speed (rpm) and rotor type on specific energy (kW.h/ton) at different concave clearances (mm). The data shown are averages of three replicates ± SD. Asterisks indicate significant differences from the round bar rotor at 5%.</p>
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<p>Damaged seed % vs. different clearances (mm) and different speeds (rpm).</p>
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<p>Effect of rotor speed (rpm) and rotor type on damage seed percentage (%) at different concave clearances (mm). The data shown are averages of three replicates ± SD. Asterisks indicate significant differences from the round bar rotor at 5%.</p>
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<p>Unthreshed seed % vs. different clearances (mm) and different speeds (rpm).</p>
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<p>Effect of rotor speed (rpm) and rotor type on unthreshed seed percentage (%) at different concave clearances (mm). The data shown are averages of three replicates ± SD. Asterisks indicate significant differences from the round bar rotor at 5%.</p>
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<p>Threshing efficiency % vs. different clearances (mm) and different speeds (rpm).</p>
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<p>Effect of rotor speed (rpm) and rotor type on the threshing efficiency (%) at different concave clearances (mm). The data shown are averages of three replicates ± SD. Asterisks indicate significant differences from the round bar rotor at 5%.</p>
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17 pages, 2842 KiB  
Article
Configuration Optimization of Temperature–Humidity Sensors Based on Weighted Hilbert–Schmidt Independence Criterion in Chinese Solar Greenhouses
by Chengbao Song, Pingzeng Liu, Xinghua Liu, Lining Liu and Yuting Yu
Agriculture 2024, 14(2), 311; https://doi.org/10.3390/agriculture14020311 - 16 Feb 2024
Cited by 1 | Viewed by 4388
Abstract
For cost-sensitive Chinese solar greenhouses (CSGs) with an uneven spatial distribution in temperature and humidity, there is a lack of effective strategies for sensor configuration that can reduce sensor usage while monitoring the microclimate precisely. A configuration strategy for integrated temperature–humidity sensors (THSs) [...] Read more.
For cost-sensitive Chinese solar greenhouses (CSGs) with an uneven spatial distribution in temperature and humidity, there is a lack of effective strategies for sensor configuration that can reduce sensor usage while monitoring the microclimate precisely. A configuration strategy for integrated temperature–humidity sensors (THSs) based on the improved weighted Hilbert–Schmidt independence criterion (HSIC) is proposed in this paper. The data independence of the THSs in different sites was analyzed based on the improved HSIC, and the selection priority of the THSs was ranked based on the weighted independence of temperature and humidity. Then, according to different cost constraints and monitoring requirements, suitable THSs could be selected sequentially and constitute the monitoring solution. Compared with the original monitoring solution containing twenty-two THSs, the optimized solution used only four THSs (S6, S9 and H6, H5) under strict cost constraints, with a maximum RMSE of the temperature and relative humidity of 0.6 °C and 2.30%, as well as a maximum information gain rate (IGR) of 9.47% and 10.0%. If higher monitoring precision is required, we can increase the THS usage with a greater budget. The optimized solution with six THSs (S6, S9, S8 and H6, H5, H2) could further reduce the maximum RMSE of the temperature and relative humidity to 0.33 °C and 1.10% and the IGR to 6.9% and 8.7%. This indicated that the proposed strategy could use much fewer THSs to achieve accurate and comprehensive monitoring, which would provide efficient and low-cost solutions for CSG microclimate monitoring. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Temperature- and humidity-monitoring system in the CSG. (<b>a</b>) Temperature and humidity data collected with IoT platform; (<b>b</b>) THS spatial layout diagram. Note: S1~S14 and H1~H8 represent the sensors located in north–south vertical direction and in east–west horizontal direction, respectively.</p>
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<p>Flow chart of THS configuration optimization.</p>
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<p>Temperature and humidity in the north–south vertical direction (9 December–6 January). (<b>a</b>) Temperature in the vertical direction; (<b>b</b>) Box chart of temperature in the vertical direction; (<b>c</b>) Relative humidity in the vertical direction; (<b>d</b>) Box chart of relative humidity in the vertical direction.</p>
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<p>Temperature and humidity in the east–west horizontal direction (9 December–6 January). (<b>a</b>) Temperature in the horizontal direction; (<b>b</b>) Box chart of temperature in the horizontal direction; (<b>c</b>) Relative humidity in the horizontal direction; (<b>d</b>) Box chart of relative humidity in the horizontal direction.</p>
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<p>The RMSE curve of temperature and humidity with the increase in THS quantity. (<b>a</b>) RMSE variation with the increase in THS quantity in vertical direction; (<b>b</b>) RMSE variation with the increase in THS quantity in horizontal direction.</p>
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<p>The IGR curve of temperature and humidity with the increase in THS quantity. (<b>a</b>) IGR variation with the increase in THS quantity in vertical direction; (<b>b</b>) IGR variation with the increase in THS quantity in horizontal direction.</p>
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<p>The temperature and relative humidity curves with different THS combinations (12 January). (<b>a</b>) The temperature curves in the vertical direction; (<b>b</b>) The relative humidity curves in the vertical direction; (<b>c</b>) The temperature curves in the horizontal direction; (<b>d</b>) The relative humidity curves in the horizontal direction.</p>
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20 pages, 8075 KiB  
Article
Experimental Study on the Soil Conditions for Rapeseed Transplanting for Blanket Seedling Combined Transplanter
by Dong Jiang, Zhuohuai Guan, Lan Jiang, Jun Wu, Qing Tang, Chongyou Wu and Yajun Cai
Agriculture 2024, 14(2), 310; https://doi.org/10.3390/agriculture14020310 - 15 Feb 2024
Cited by 1 | Viewed by 1233
Abstract
To address the lack of available information on the soil physical conditions suitable for rapeseed blanket-shaped seedling transplanting, as well as the lack of protocols for the optimisation of soil tillage components in the utilisation of an integrated rapeseed blanket seedling combined transplanter, [...] Read more.
To address the lack of available information on the soil physical conditions suitable for rapeseed blanket-shaped seedling transplanting, as well as the lack of protocols for the optimisation of soil tillage components in the utilisation of an integrated rapeseed blanket seedling combined transplanter, the physical parameters of different soil conditions and their impact on the growth of rapeseed after transplanting were investigated in this study. The aim was to determine the suitable soil physical parameters for rapeseed blanket-shaped seedling transplanting. First, the changes in soil firmness, soil bulk density, and soil moisture content during the installation of the rapeseed blanket seedling combined transplanter were tested and analysed, providing preliminary data for subsequent research. Using the variables of soil firmness and soil moisture content in the micro-environment around the roots and stems (30–50 mm) after rapeseed seedling transplantation and indicators such as the survival rate, root diameter, seedling height, and dry weight, an experiment on the growth of rapeseed blanket-shaped seedlings was conducted based on the furrow cutting transplanting principle. The results indicated that during the initial stage of rapeseed transplanting, the soil moisture content significantly influenced the vitality of the rapeseed plants. Under a high soil moisture content, the typically lengthy seedling period was shortened, and the effect on vitality was good, with minimal influence from the soil firmness. After seedling establishment, the rapeseed growth was significantly affected by the soil firmness. When the soil moisture content was less than 20%, increasing the soil firmness to 500 kPa was beneficial for moisture retention and rapeseed seedling growth. At a soil moisture content ranging from 20 to 25%, a soil firmness of 400 kPa was most suitable for both rapeseed vitality and late-stage growth. When the soil moisture content exceeded 25%, reducing the soil firmness to 300 kPa was beneficial for rapeseed growth, as an excessively high moisture content may lead to soil compaction, affecting seedling development. This study provides a theoretical basis for optimizing the design of soil tillage components in the application of an integrated rapeseed blanket seedling combined transplanter and for the high-yield management of rapeseed after transplanting. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Microenvironmental soil conditions around roots and stems of rapeseed plants after transplantation. (h) Depth. (r) Radius.</p>
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<p>Soil compaction test.</p>
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<p>2ZGK-6 model rapeseed blanket seedling combined transplanter. The identifier is the manufacturer’s logo for this device.</p>
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<p>Seedbed preparation processes. These are listed as follows: (<b>a</b>) furrow opening; (<b>b</b>) laying the preprepared soil into the furrows; (<b>c</b>) levelling of the furrow ridges; (<b>d</b>) final slits (square-bottomed planting furrows).</p>
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<p>Morphological characteristics of rapeseed seedling plants. (h) Seedling height. (d) Stem diameter.</p>
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<p>Soil compression test results. These are listed as follows: (<b>a</b>) soil firmness; (<b>b</b>) soil bulk density.</p>
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<p>Soil firmness under different soil conditions before and after transplanting.</p>
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<p>Changes in soil firmness two weeks after transplanting rapeseed blanket seedlings under Different soil moisture contents. These are listed as follows: (<b>a</b>) Experiment 1 (Nanjing); (<b>b</b>) Experiment 2 (Liyang).</p>
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<p>Growth statuses of rapeseed blanket seedlings two weeks after transplantation under different soil conditions. These are listed as follows: (<b>a</b>) Experiment 1 (Nanjing); (<b>b</b>) Experiment 2 (Liyang).</p>
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<p>Growth of rapeseed blanket seedlings during the initial transplanting stage under different soil conditions. These are listed as follows: (<b>a</b>) Experiment 1 (Nanjing); (<b>b</b>) Experiment 2 (Liyang).</p>
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<p>After soil compaction, the rapeseed root system is unable to penetrate the soil.</p>
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<p>The impact of soil compaction on the growth of rapeseed roots after transplanting. The soil types are listed as follows: (<b>a</b>) noncompacted soil; (<b>b</b>) compacted soil.</p>
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16 pages, 6575 KiB  
Article
Ecological Risk Assessment and Source Analysis of Heavy Metals in Farmland Soil in Yangchun City Based on APCS-MLR and Geostatistics
by Yingyuting Li, Yili Zhang, Junyu Chen, Guangfei Yang, Haihui Li, Jinjin Wang and Wenyan Li
Agriculture 2024, 14(2), 309; https://doi.org/10.3390/agriculture14020309 - 15 Feb 2024
Viewed by 1070
Abstract
Yangchun City, a typical polymetallic ore distribution area in Guangdong Province (China), was selected as the research region to study the content, distribution, source, and possible impacts of heavy metals (HMs) (Arsenic: As; Cadmium: Cd; Chromium: Cr; Copper: Cu; Mercury: Hg; Nickel: Ni; [...] Read more.
Yangchun City, a typical polymetallic ore distribution area in Guangdong Province (China), was selected as the research region to study the content, distribution, source, and possible impacts of heavy metals (HMs) (Arsenic: As; Cadmium: Cd; Chromium: Cr; Copper: Cu; Mercury: Hg; Nickel: Ni; Lead: Pb; and Zinc: Zn) on the farmland soil of this City. According to our findings, the spatial distribution of HMs in Yangchun City shows higher concentrations in the north and southeast and lower in the west and other regions. Metal content in some sampled sites of the agricultural land exceeded the soil pollution risk screening values, particularly As (7.5%), Cd (12%), Cu (4%), Hg (14.5%), and Pb (3%). Additionally, the average content of As, Cu, Cd, Pb, Hg, and Zn from the studied areas surpassed the soil background value of Guangdong Province for all metals. The absolute principal component score-multiple linear regression (APCS-MLR) was used to identify potential sources of HMs in the soil samples. There are three potential sources identified by the model: traffic emissions, natural sources, and agricultural activities, accounting for 28.16%, 16.68%, and 14.42%, respectively. Based on the ecological risk assessment, the potential ecological risk (Eri = 310.77), Nemero pollution index (PN = 2.27), and multiple possible effect concentration quality (mPECQs = 0.23) indicated that the extent of heavy metal pollution in the soil samples was moderate. Three sources were identified: traffic emissions, natural sources, and agricultural activities. We suggest that by combining the above results, a monitoring and early warning system focused on Cd and Hg can be established. The system could utilize geographic information systems and remote sensing technologies to achieve dynamic monitoring and prediction of pollution. Regular testing of soils and sustainable management practices are also recommended to control and remediate contamination. Full article
(This article belongs to the Special Issue Heavy Metals in Farmland Soils: Mechanisms and Remediation Strategies)
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<p>Distribution of farmland soil sampling points and Mineral hotspots in Yangchun City.</p>
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<p>Box plots of soil heavy metal content in Yangchun City.</p>
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<p>Spatial Distribution Map of Soil Heavy Metal Content in Yangchun City.</p>
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<p>Spatial Distribution Map of Soil Heavy Metal Content in Yangchun City.</p>
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<p>Correlation matrix of physicochemical properties and heavy metal concentrations in farmland soil.</p>
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<p>The soil heavy metal pollution sources’ contribution rate in the research region.</p>
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23 pages, 7956 KiB  
Article
Simulation Analysis and Multiobjective Optimization of Pulverization Process of Seed-Used Watermelon Peel Pulverizer Based on EDEM
by Xiaobin Mou, Fangxin Wan, Jinfeng Wu, Qi Luo, Shanglong Xin, Guojun Ma, Xiaoliang Zhou, Xiaopeng Huang and Lizeng Peng
Agriculture 2024, 14(2), 308; https://doi.org/10.3390/agriculture14020308 - 15 Feb 2024
Cited by 4 | Viewed by 1221
Abstract
To enhance the utilization of seed-used watermelon peel and mitigate environmental pollution, a hammer-blade seed-used watermelon peel crusher was designed and manufactured, and its structure and working parameters were optimized. Initially, the seed-used watermelon peel crusher and seed-used watermelon peel model were constructed, [...] Read more.
To enhance the utilization of seed-used watermelon peel and mitigate environmental pollution, a hammer-blade seed-used watermelon peel crusher was designed and manufactured, and its structure and working parameters were optimized. Initially, the seed-used watermelon peel crusher and seed-used watermelon peel model were constructed, and the model’s parameters were calibrated. Subsequently, the discrete element method (EDEM2022) was employed to investigate the effects of spindle speed (MSS), the number of hammers (NCB), and feeding volume (FQ) on the pulverizing process. Multivariate nonlinear regression prediction models were developed for the percentage of pulverized particle size less than 8 mm (Psv), pulverizing efficiency (Ge), and power density (Ppd), followed by the analysis of influencing factors and prediction models using ANOVA. The multiobjective optimization of the prediction model utilizing the improved hybrid metacellular genetic algorithm CellDE resulted in solutions of 90.02%, 89.57%, and 8.35 × 10−3 t/(h-kw) for Psv-opt, Ge-opt, and Ppd-opt, respectively. The corresponding optimal interaction values of MSS, NCB, and FQ were determined to be 1500 r/min, 108, and 150 kg/min. Finally, a prototype test was conducted by combining the optimal factor interaction values, yielding statistically calculated values of 96.63%, 92.37%, and 7.76 × 10−3 t/(h-kw) for Psv-pr, Ge-pr, and Ppd-pr, respectively. The results indicate that the optimized values of Psv-opt, Ge-opt, and Ppd-opt models have an error of less than 8% compared to the statistically calculated values of the prototype test and outperform the values of Psv-ori, Ge-ori, and Ppd-ori obtained under the original parameters. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Test sample: (<b>a</b>) seed-used watermelon peel structure diagram, (<b>b</b>) seed-used watermelon model diagram, (<b>c</b>) seed-used watermelon peel sample. (1) Outer layer of seed-used watermelon peel, (2) seed-used watermelon peel middle layer, (3) seed-used watermelon peel lining.</p>
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<p>Seed−used watermelon peel puncture test force−displacement diagram. (<b>a</b>) Outer layer of seed-used watermelon peel, (<b>b</b>) seed−used watermelon peel middle layer, (<b>c</b>) seed−used watermelon peel lining.</p>
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<p>TPA compression force−time plot. (<b>a</b>) Outer layer of seed-used watermelon peel, (<b>b</b>) seed−used watermelon peel middle layer, (<b>c</b>) seed−used watermelon peel lining.</p>
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<p>The parameter calibration test. (<b>a</b>) TPA cyclic compression test, (<b>b</b>) self-made photoelectric inductive measuring stage for static friction coefficient. (1) Bottom plate; (2) protractor; (3) tilt plate (800 mm × 600 mm); (4) photoelectric switch.</p>
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<p>Structure diagram of the whole machine. (<b>a</b>) Three-dimensional diagram: (1) power plant, (2) transmission shaft, (3) crushing chamber, (4) machine base; (<b>b</b>) two-dimensional diagram: (1) motor, (2) elastic pin coupling, (3) bearings, (4) bearing seat, (5) principal axis, (6) airframe structure, (7) sealing gasket, (8) active end cover, (9) gland cover, (10) axis pin, (11) spacer of crushing hammers, (12) hammers, (13) circlip for shaft, (14) base support.</p>
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<p>Seed-used watermelon peel discrete element model. (<b>a</b>) Crushing model, (<b>b</b>) model of the particles to be crushed generated by bonding.</p>
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<p>Crushing simulation process.</p>
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<p>CellDE algorithm schematic diagram.</p>
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<p>The crushing of seed and melon peels at different moments. (<b>a</b>) <span class="html-italic">t</span> = 0.5 s, (<b>b</b>) <span class="html-italic">t</span> = 1.0 s.</p>
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<p>Time plot of each metric during simulation time. (<b>a</b>) Compression force−time curve of seed−used watermelon peel, (<b>b</b>) parallel bond normal force−time curve, (<b>c</b>) hammer blade pressure–time curve, (<b>d</b>) number of bond breaks–time curve.</p>
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<p><span class="html-italic">P</span><sub>sv</sub> curve of percentage of particle size less than 8 mm. (<b>a</b>) Spindle speed, (<b>b</b>) hammer number, (<b>c</b>) feeding rate, (<b>d</b>) feeding rate–spindle speed.</p>
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<p>Crushing efficiency Ge curve diagram. (<b>a</b>) Spindle speed, (<b>b</b>) hammer number, (<b>c</b>) feeding rate, (<b>d</b>) hammer number–spindle speed.</p>
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<p>Power density <span class="html-italic">P</span><sub>pd</sub> curve. (<b>a</b>) Spindle speed, (<b>b</b>) hammer number, (<b>c</b>) feeding rate, (<b>d</b>) feeding rate−spindle speed.</p>
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<p>Pareto frontier curve: (<b>a</b>) The Pareto front of the target value obtained by CellDE, (<b>b</b>) Pareto front of parameter values obtained by CellDE.</p>
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<p>Experimental prototype. (<b>a</b>) Test prototype; (<b>b</b>) seed-used watermelon peels used in the experiment.</p>
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<p>The crushing effect of the seed-used watermelon rind. (<b>a</b>) The crushing effect under the original parameters; (<b>b</b>) the crushing effect after parameter optimization.</p>
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18 pages, 2589 KiB  
Article
Trichoderma Biocontrol Performances against Baby-Lettuce Fusarium Wilt Surveyed by Hyperspectral Imaging-Based Machine Learning and Infrared Thermography
by Gelsomina Manganiello, Nicola Nicastro, Luciano Ortenzi, Federico Pallottino, Corrado Costa and Catello Pane
Agriculture 2024, 14(2), 307; https://doi.org/10.3390/agriculture14020307 - 14 Feb 2024
Cited by 1 | Viewed by 1556
Abstract
Fusarium oxysporum f. sp. lactucae is one of the most aggressive baby-lettuce soilborne pathogens. The application of Trichoderma spp. as biocontrol agents can minimize fungicide treatments and their effective targeted use can be enhanced by support of digital technologies. In this work, two [...] Read more.
Fusarium oxysporum f. sp. lactucae is one of the most aggressive baby-lettuce soilborne pathogens. The application of Trichoderma spp. as biocontrol agents can minimize fungicide treatments and their effective targeted use can be enhanced by support of digital technologies. In this work, two Trichoderma harzianum strains achieved 40–50% inhibition of pathogen radial growth in vitro. Their effectiveness in vivo was surveyed by assessing disease incidence and severity and acquiring hyperspectral and thermal features of the canopies being treated. Infected plants showed a reduced light absorption in the green and near-red regions over time, reflecting the disease progression. In contrast, Trichoderma-treated plant reflectance signatures, even in the presence of the pathogen, converged towards the healthy control values. Seventeen vegetation indices were selected to follow disease progression. The thermographic data were informative in the middle–late stages of disease (15 days post-infection) when symptoms were already visible. A machine-learning model based on hyperspectral data enabled the early detection of the wilting starting from 6 days post-infection, and three different spectral regions sensitive to baby-lettuce wilting (470–490 nm, 740–750 nm, and 920–940 nm) were identified. The obtained results pioneer an effective AI-based decision support system (DSS) for crop monitoring and biocontrol-based management. Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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<p>Representative photographs of the four baby-lettuce Fusarium wilt disease classes (0–3 severity scale).</p>
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<p>Radial growth inhibition (%) of <span class="html-italic">F. oxysporum f. sp. lactucae</span> challenged in vitro with <span class="html-italic">T. harzianum</span> Ts (green) and T2 (blue) strains, assessed daily for 144 h after plate inoculation. Different letters indicate significant differences between treatments (<span class="html-italic">p</span> ≤ 0.05) according to ordinary two-way ANOVA followed by a Bonferroni’s multiple comparison test.</p>
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<p>Effect of <span class="html-italic">T. harzianum</span> T2 (blue) and Ts (green) strains on incidence (<b>A</b>) and severity (<b>B</b>) of baby lettuce wilting caused by <span class="html-italic">F. oxysporum f. sp. lactucae</span> compared to the infected untreated control (red); monitored every three days for 18 days post-pathogen inoculation. Different letters indicate significant differences between treatments (<span class="html-italic">p</span> ≤ 0.05) according to ordinary one-way ANOVA followed by a Bonferroni’s multiple comparison test.</p>
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<p>Biometric parameters, including leaf area (<b>A</b>), and plant fresh weight (<b>B</b>), root fresh and dry weights (<b>C</b>–<b>E</b>), and stem fresh and dry weights (<b>D</b>–<b>F</b>), assessed at the end of the in vivo experiment for untreated healthy (H) and infected (<span class="html-italic">Fol</span>) controls, plants treated with <span class="html-italic">T. harzianum</span> T2 and Ts strains, and their combinations with <span class="html-italic">Fol</span>. Different letters indicate significant differences between treatments (<span class="html-italic">p</span> ≤ 0.05) according to ordinary one-way ANOVA followed by a Bonferroni’s multiple comparison test.</p>
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<p>Thermal images (<b>A</b>) and trend over time of the thermographic parameter ΔT (<b>B</b>) during the in vivo experiment in untreated healthy (H) and infected (<span class="html-italic">Fol</span>) controls (CTRL), and treatments with <span class="html-italic">T. harzianum</span> T2 and Ts strains, and their combinations with <span class="html-italic">Fol</span>. Different letters indicate significant differences between treatments (<span class="html-italic">p</span> ≤ 0.05) according to ordinary one-way ANOVA followed by a Bonferroni’s multiple comparison test. Hyperspectral (400–1000 nm) reflectance signatures (<b>C</b>) acquired during the in vivo experiment for untreated healthy (H) and infected (<span class="html-italic">Fol</span>) controls, and treatments with <span class="html-italic">T. harzianum</span> T2 and Ts strains, and their combinations with <span class="html-italic">Fol</span> at each time point (3 to 18 dpi).</p>
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<p>Principal component analysis biplot for visualizing distribution of healthy (H) and infected (Fol) controls, treatments with T. harzianum T2 and Ts strains, and their combinations with Fol at each assessment (3 to 18 dpi—I to VI), for the 16 selected vegetation indices (variables). X and Y axis show principal component 1 and principal component 2, explaining 70.7% and 16.9% of the total vari-ance, respectively. Color grouping was performed by considering the disease severity classes in the range 0 = healthy to 4 = highly infected.</p>
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<p>VIS–NIR spectral reflectance mean values of healthy (blue) and <span class="html-italic">Fol</span>-infected (orange) plant samples. Normalized feature importance values (grey) of the machine learning model are reported on the right axis.</p>
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22 pages, 3871 KiB  
Review
Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review
by Weihong Ma, Xiangyu Qi, Yi Sun, Ronghua Gao, Luyu Ding, Rong Wang, Cheng Peng, Jun Zhang, Jianwei Wu, Zhankang Xu, Mingyu Li, Hongyan Zhao, Shudong Huang and Qifeng Li
Agriculture 2024, 14(2), 306; https://doi.org/10.3390/agriculture14020306 - 14 Feb 2024
Cited by 1 | Viewed by 2908
Abstract
Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential [...] Read more.
Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement. Full article
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<p>Computer vision-based phenotypic data acquisition technical framework.</p>
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<p>Three different 3D reconstruction methods based on RGB images, laser scanning, and 3D cameras. (<b>a</b>) displays a 3D reconstruction technique utilizing RGB images [<a href="#B29-agriculture-14-00306" class="html-bibr">29</a>]; (<b>b</b>,<b>c</b>) shows two different 3D reconstruction methods based on laser scanning [<a href="#B30-agriculture-14-00306" class="html-bibr">30</a>,<a href="#B31-agriculture-14-00306" class="html-bibr">31</a>]; (<b>d</b>) is reconstructed using a 3D camera [<a href="#B32-agriculture-14-00306" class="html-bibr">32</a>]. These techniques provide innovative computer vision methods for precise, non-contact measurement of livestock body dimensions and weight.</p>
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<p>Acquisition method and the result of 3D reconstruction in [<a href="#B31-agriculture-14-00306" class="html-bibr">31</a>,<a href="#B42-agriculture-14-00306" class="html-bibr">42</a>], (<b>a</b>) is the result of 3D reconstruction in [<a href="#B31-agriculture-14-00306" class="html-bibr">31</a>], (<b>b</b>) is the result of 3D reconstruction in [<a href="#B42-agriculture-14-00306" class="html-bibr">42</a>].</p>
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<p>Depth camera-based 3D reconstruction methods. (<b>a</b>,<b>b</b>) The scenes where the point clouds are acquired in [<a href="#B46-agriculture-14-00306" class="html-bibr">46</a>,<a href="#B49-agriculture-14-00306" class="html-bibr">49</a>], respectively. (<b>c</b>,<b>d</b>) The 3D reconstruction methods in [<a href="#B46-agriculture-14-00306" class="html-bibr">46</a>,<a href="#B49-agriculture-14-00306" class="html-bibr">49</a>], respectively.</p>
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<p>Livestock body dimension acquisition technology.</p>
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<p>The geometric segmentation methods based on RGB images. (<b>a</b>,<b>b</b>) The scenarios of acquiring point cloud data in [<a href="#B55-agriculture-14-00306" class="html-bibr">55</a>,<a href="#B65-agriculture-14-00306" class="html-bibr">65</a>], respectively. (<b>c</b>,<b>d</b>) The key point detection in [<a href="#B55-agriculture-14-00306" class="html-bibr">55</a>,<a href="#B65-agriculture-14-00306" class="html-bibr">65</a>], respectively.</p>
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<p>Neural network-based key area segmentation [<a href="#B56-agriculture-14-00306" class="html-bibr">56</a>]. Subfigure (<b>a</b>) shows the determined ear–root point pairs and tail–root point pairs. Subfigure (<b>b</b>) depicts a schematic of the backline in a planar projection. Subfigure (<b>c</b>) presents the results after segmenting the pig’s body.</p>
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<p>Brief framework of neural network-based point cloud weight estimation method.</p>
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<p>Neural network weight estimation process [<a href="#B77-agriculture-14-00306" class="html-bibr">77</a>].</p>
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9 pages, 978 KiB  
Article
Efficacy and Phytotoxicity of Sulfur Dioxide Fumigation for Postharvest Control of Western Flower Thrips, Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae), on Select Fresh Fruit and Vegetables
by Yong-Biao Liu
Agriculture 2024, 14(2), 305; https://doi.org/10.3390/agriculture14020305 - 14 Feb 2024
Cited by 1 | Viewed by 1389
Abstract
Sulfur dioxide (SO2) fumigation was evaluated for efficacy against western flower thrips (Frankliniella occidentalis) and phytotoxicity to four select fresh fruits and vegetables. Western flower thrips were found to be very susceptible to SO2 fumigation. Fumigations with 0.3 [...] Read more.
Sulfur dioxide (SO2) fumigation was evaluated for efficacy against western flower thrips (Frankliniella occidentalis) and phytotoxicity to four select fresh fruits and vegetables. Western flower thrips were found to be very susceptible to SO2 fumigation. Fumigations with 0.3 and 0.5% SO2 for 60 and 30 min, respectively, at a low temperature of 5 °C achieved 100% thrips mortality. Broccoli, bell peppers, apples, and navel oranges with thrips were subjected to 30 min fumigation with 0.3–0.5% SO2 to verify efficacy and determine potential phytotoxicity. The fumigation resulted in complete control of thrips. Its effects on visual quality of fresh produce varied. The fumigation caused severe discoloration of broccoli. However, the treatment did not have significant effects on the color of other products. No negative impact on visual appearance of bell peppers and navel oranges was observed. However, it caused darkened lenticels on green apples and, therefore, may potentially degrade apple postharvest quality. The lack of phytotoxicity of SO2 fumigation is likely due to well-developed wax layers on those fresh products. The results of the study suggest that SO2 fumigation has good potential to be used safely and effectively against sensitive pests on select fresh fruit and vegetables including peppers and citrus fruits. Full article
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<p>Appearance of broccoli, green and red bell peppers, green and yellow apples, and navel oranges from 30-min fumigation treatment with 0.3–0.5% SO2 at 5 °C (T) and unfumigated controls (C) at one week (broccoli and peppers) and two weeks (apples and navel oranges) after fumigation.</p>
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<p>Color appearances of green apples, yellow apples, navel oranges, green peppers, and red peppers from SO<sub>2</sub> fumigations and controls based on <span class="html-italic">L</span>*, <span class="html-italic">a</span>*, and <span class="html-italic">b</span>* values in <a href="#agriculture-14-00305-t002" class="html-table">Table 2</a> using an online color converter (<a href="https://www.nixsensor.com/free-color-converter/" target="_blank">https://www.nixsensor.com/free-color-converter/</a>, accessed on 12 February 2024).</p>
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21 pages, 6743 KiB  
Article
Chitosan Regulates the Root Architecture System, Photosynthetic Characteristics and Antioxidant System Contributing to Salt Tolerance in Maize Seedling
by Qiujuan Jiao, Fengmin Shen, Lina Fan, Zihao Song, Jingjing Zhang, Jia Song, Shah Fahad, Fang Liu, Ying Zhao, Zhiqiang Tian and Haitao Liu
Agriculture 2024, 14(2), 304; https://doi.org/10.3390/agriculture14020304 - 14 Feb 2024
Cited by 3 | Viewed by 1487
Abstract
Salinity is an obstacle to global agriculture, as it affects plant growth and development. Chitosan (CTS) has been suggested as a plant growth regulator to alleviate environmental stresses. In this study, the morphological and biochemical responses of chitosan application (75 mg L−1 [...] Read more.
Salinity is an obstacle to global agriculture, as it affects plant growth and development. Chitosan (CTS) has been suggested as a plant growth regulator to alleviate environmental stresses. In this study, the morphological and biochemical responses of chitosan application (75 mg L−1) on maize seedling growth under salt stress (150 mM) were conducted with a hydroponic experiment. The results exhibited that CTS application effectively recovered salt-inhibited biomass accumulation and root architecture by increasing chlorophyll content and photosynthetic assimilation and reducing sodium content in shoots and roots by 25.42% and 5.12% compared with NaCl treatment. Moreover, salt-induced oxidative stress was alleviated by CTS application by increasing the activities of antioxidant enzymes of superoxide dismutase, catalase, ascorbate peroxidase, peroxidase and content of ascorbate. Correlation analysis and partial least squares (PLS) analysis revealed that root morphology and ascorbate play key roles for maize seedlings in response to salt stress. Based on these results, CTS application is recommended as an effective approach to enhance the tolerance of maize seedlings under salt stress. Full article
(This article belongs to the Section Crop Production)
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<p>Effect of exogenous chitosan (CTS) on maize seedlings growth phenotype and root morphology under salt stress. CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>Effect of exogenous chitosan (CTS) on maize seedlings percentage of root morphology (RL, SA, RV) under salt stress. CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>Effect of exogenous chitosan (CTS) on Na concentration in leaf (<b>A</b>) and root (<b>B</b>) of maize seedlings under salt stress. The significance differences between treatments were assessed using one-way ANOVA. Lowercase letters positioned above bars indicated the significant difference among treatments at the level of <span class="html-italic">p</span> &lt; 0.05. CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>Effect of chitosan (CTS) on photosynthetic parameters (Pn (<b>A</b>), Gs (<b>B</b>), Ci (<b>C</b>), Tr (<b>D</b>)), Ls (<b>E</b>) and chlorophyll content (Chla (<b>F</b>), Chlb (<b>G</b>), TChl (<b>H</b>)) of maize seedling under salt stress. The significance differences between treatments were assessed using one-way ANOVA. Lowercase letters positioned above bars indicated the significant difference among treatments at the level of <span class="html-italic">p</span> &lt; 0.05. CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>Effect of chitosan (CTS) on relative electrolyte conductivity (<b>A</b>,<b>B</b>) and malondialdehyde (MDA) (<b>C</b>,<b>D</b>) content of maize seedling in leaf and root tissue under salt stress. The significance differences between treatments were assessed using one-way ANOVA. Lowercase letters positioned above bars indicated the significant difference among treatments at the level of <span class="html-italic">p</span> &lt; 0.05. CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>Effect of exogenous chitosan (CTS) on the ascorbate (AsA) content of maize seedling in leaf (<b>A</b>) and root (<b>B</b>) tissue under salt stress. The significance differences between treatments were assessed using one-way ANOVA. Lowercase letters positioned above bars indicated the significant difference among treatments at the level of <span class="html-italic">p</span> &lt; 0.05. CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>Effect of exogenous chitosan (CTS) on soluble protein content (<b>A</b>,<b>B</b>) and the SOD (<b>C</b>,<b>D</b>), CAT (<b>E</b>,<b>F</b>), APX (<b>G</b>,<b>H</b>), and POD (<b>I</b>,<b>J</b>) activities of maize seedling in leaf and root tissue under salt stress. The significance differences between treatments were assessed using one-way ANOVA. Lowercase letters positioned above bars indicated the significant difference among treatments at the level of <span class="html-italic">p</span> &lt; 0.05. CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>The primary component of primary component analysis (PCA) of indexes of maize seedlings induced by exogenous chitosan (CTS) under salt stress. In there symbols, “triangle” displayed the CK treatment, “plus” presented the CTS group, “x” showed the S treatment, “square” exhibited the CTS + S group. CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>Primary component analysis (PCA) results of treatments (<b>A</b>) and indexes (<b>B</b>) of maize seedlings induced by exogenous chitosan (CTS) under salt stress. Shoot FW: shoot fresh weight; Root FW: root fresh weight; Shoot DW: shoot dry weight; Root DW: root dry weight; Shoot Ti: shoot tolerance index; Root Ti: root tolerance index; RL: total root length; SA: root surface area; RD: root average diameter; RV: root volume; RT: root tips; RF: root forks; I: 0 &lt; RD &lt; 0.5 mm; II: 0.5 &lt; RD &lt; 1.0 mm; III: 1.0 &lt; RD &lt; 1.5 mm; IV: RD &gt; 1.5 mm; Chla: chlorophyll a; Chlb: chlorophyll b; TChl: total chlorophyll; Pn: photosynthetic rate; Gs: stomatal conductance; Ci: intercellular CO<sub>2</sub> concentration; Tr: transpiration rate; Ls: stomatal limitation; REC: relative electrolyte conductivity; MDA: malondialdehyde; AsA: ascorbate; SOD: superoxide dismutase; CAT: catalase; APX: ascorbate peroxidase; POD: peroxidase; CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>Correlation analysis depicting the relationship between various indices of maize seedlings subjected to exogenous CTS under salt stress condition. Shoot FW: shoot fresh weight; Root FW: root fresh weight; Shoot DW: shoot dry weight; Root DW: root dry weight; Shoot Ti: shoot tolerance index; Root Ti: root tolerance index; RL: total root length; SA: root surface area; RD: root average diameter; RV: root volume; RT: root tips; RF: root forks; I: 0 &lt; RD &lt; 0.5 mm; II: 0.5 &lt; RD &lt; 1.0 mm; III: 1.0 &lt; RD &lt; 1.5 mm; IV: RD &gt; 1.5 mm; Chla: chlorophyll a; Chlb: chlorophyll b; TChl: total chlorophyll; Pn: photosynthetic rate; Gs: stomatal conductance; Ci: intercellular CO<sub>2</sub> concentration; Tr: transpiration rate; Ls: stomatal limitation; REC: relative electrolyte conductivity; MDA: malondialdehyde; AsA: ascorbate; SOD: superoxide dismutase; CAT: catalase; APX: ascorbate peroxidase; POD: peroxidase.</p>
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<p>Heatmap analysis depicting the response of maize seedlings to exogenous CTS under salt stress condition. Shoot FW: shoot fresh weight; Root FW: root fresh weight; Shoot DW: shoot dry weight; Root DW: root dry weight; Shoot Ti: shoot tolerance index; Root Ti: root tolerance index; RL: total root length; SA: root surface area; RD: root average diameter; RV: root volume; RT: root tips; RF: root forks; I: 0 &lt; RD &lt; 0.5 mm; II: 0.5 &lt; RD &lt; 1.0 mm; III: 1.0 &lt; RD &lt; 1.5 mm; IV: RD &gt; 1.5 mm; Chla: chlorophyll a; Chlb: chlorophyll b; TChl: total chlorophyll; Pn: photosynthetic rate; Gs: stomatal conductance; Ci: intercellular CO<sub>2</sub> concentration; Tr: transpiration rate; Ls: stomatal limitation; REC: relative electrolyte conductivity; MDA: malondialdehyde; AsA: ascorbate; SOD: superoxide dismutase; CAT: catalase; APX: ascorbate peroxidase; POD: peroxidase; CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>Application of random forest analysis to assess the response of maize seedlings to exogenous CTS under salt stress conditions. RL: total root length; SA: root surface area; RD: root average diameter; RV: root volume; II: 0.5 &lt; RD &lt; 1.0 mm; III: 1.0 &lt; RD &lt; 1.5 mm; IV: RD &gt; 1.5 mm; TChl: total chlorophyll; MDA: malondialdehyde; AsA: ascorbate; CK: control; CTS: 75 mg L<sup>−1</sup> CTS; S: 150 mM NaCl; CTS + S: 75 mg L<sup>−1</sup> CTS + 150 mM NaCl.</p>
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<p>The variable importance for projection (VIP) values derived from the partial least squares (PLS) model and the key factors influencing shoot tolerance (<b>A</b>) and root tolerance (<b>B</b>) in maize seedlings subjected to exogenous CTS under salt stress condition. RL: total root length; SA: root surface area; RD: root average diameter; RV: root volume; RT: root tips; RF: root forks; I: 0 &lt; RD &lt; 0.5 mm; II: 0.5 &lt; RD &lt; 1.0 mm; III: 1.0 &lt; RD &lt; 1.5 mm; IV: RD &gt; 1.5 mm; Chla: chlorophyll a; Chlb: chlorophyll b; TChl: total chlorophyll; Pn: photosynthetic rate; Gs: stomatal conductance; Ci: intercellular CO<sub>2</sub> concentration; Tr: transpiration rate; Ls: stomatal limitation; REC: relative electrolyte conductivity; MDA: malondialdehyde; AsA: ascorbate; SOD: superoxide dismutase; CAT: catalase; APX: ascorbate peroxidase; POD: peroxidase.</p>
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16 pages, 12037 KiB  
Article
Optimizing the YOLOv7-Tiny Model with Multiple Strategies for Citrus Fruit Yield Estimation in Complex Scenarios
by Juanli Jing, Menglin Zhai, Shiqing Dou, Lin Wang, Binghai Lou, Jichi Yan and Shixin Yuan
Agriculture 2024, 14(2), 303; https://doi.org/10.3390/agriculture14020303 - 13 Feb 2024
Cited by 3 | Viewed by 1448
Abstract
The accurate identification of citrus fruits is important for fruit yield estimation in complex citrus orchards. In this study, the YOLOv7-tiny-BVP network is constructed based on the YOLOv7-tiny network, with citrus fruits as the research object. This network introduces a BiFormer bilevel routing [...] Read more.
The accurate identification of citrus fruits is important for fruit yield estimation in complex citrus orchards. In this study, the YOLOv7-tiny-BVP network is constructed based on the YOLOv7-tiny network, with citrus fruits as the research object. This network introduces a BiFormer bilevel routing attention mechanism, which replaces regular convolution with GSConv, adds the VoVGSCSP module to the neck network, and replaces the simplified efficient layer aggregation network (ELAN) with partial convolution (PConv) in the backbone network. The improved model significantly reduces the number of model parameters and the model inference time, while maintaining the network’s high recognition rate for citrus fruits. The results showed that the fruit recognition accuracy of the modified model was 97.9% on the test dataset. Compared with the YOLOv7-tiny, the number of parameters and the size of the improved network were reduced by 38.47% and 4.6 MB, respectively. Moreover, the recognition accuracy, frames per second (FPS), and F1 score improved by 0.9, 2.02, and 1%, respectively. The network model proposed in this paper has an accuracy of 97.9% even after the parameters are reduced by 38.47%, and the model size is only 7.7 MB, which provides a new idea for the development of a lightweight target detection model. Full article
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<p>Data enhancement chart. (<b>a</b>) Mosaic enhancement; (<b>b</b>,<b>c</b>) Mixup enhancement; (<b>d</b>) Mosaic enhancement and gray scaling; (<b>e</b>) Gray scaling; (<b>f</b>) Gamma transform.</p>
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<p>Diagram of the YOLOv7-tiny network.</p>
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<p>Diagram of the YOLOv7-tiny-BVP network.</p>
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<p>Diagram of the BiFormer attention mechanism.</p>
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<p>Diagram of the GSConv structure.</p>
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<p>Structural diagram of the VoVGSCSPC.</p>
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<p>Diagram of the PConv structure.</p>
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<p>Mild occlusion recognition results.</p>
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<p>Heavy occlusion recognition results.</p>
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<p>Recognition effect in a dark scene.</p>
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<p>Recognition results in bright scenes.</p>
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<p>Ablation experiment.</p>
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<p>Fruit identification results of citrus fruit trees from a side view. (<b>a</b>–<b>e</b>) Results of different fruit tree tests.</p>
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22 pages, 2089 KiB  
Article
The Effects of Planting Density and Nitrogen Application on the Growth Quality of Alfalfa Forage in Saline Soils
by Jiao Liu, Faguang Lu, Yiming Zhu, Hao Wu, Irshad Ahmad, Guichun Dong, Guisheng Zhou and Yanqing Wu
Agriculture 2024, 14(2), 302; https://doi.org/10.3390/agriculture14020302 - 13 Feb 2024
Cited by 2 | Viewed by 1277
Abstract
Soil salinization has become one of the major abiotic stresses limiting agricultural production globally. The full utilization of coastal saline-alkali land is of great significance for agricultural development. Among them, fertilizer management and planting density are crucial for promoting crop growth and productivity [...] Read more.
Soil salinization has become one of the major abiotic stresses limiting agricultural production globally. The full utilization of coastal saline-alkali land is of great significance for agricultural development. Among them, fertilizer management and planting density are crucial for promoting crop growth and productivity in saline soils. Field experiments were conducted to study the effects of different nitrogen application rates and planting densities on the growth, yield, and quality of alfalfa. Using alfalfa variety WL919 as the experimental material, three seeding rates of 15.0 kg·ha−1 (D1), 30.0 kg·ha−1 (D2), and 45.0 kg·ha−1 (D3) as well as three nitrogen application rates of 150.0 kg·ha−1 (N1), 225.0 kg·ha−1 (N2), and 300.0 kg·ha−1 (N3) were set. The results showed that under the same density, different nitrogen application rates had a positive impact on the agronomic traits and yield of alfalfa on saline-alkali land. Physiological and biochemical properties (chlorophyll and sucrose) increased with increasing nitrogen application, and (starch) increased initially and then decreased with increasing nitrogen application. Forage quality attributes (crude protein and crude ash) had a significant impact, while crude fat had no significant effect. Under the same nitrogen application, the yield of alfalfa increased with increasing density but then decreased after reaching a peak, while other traits initially increased and then decreased. In conclusion, the nitrogen fertilizer was superior in promoting alfalfa growth, biomass yield, and forage yield, while planting density was more suitable at D2. Although both D2N2 and D2N3 treatments were superior to others, considering economic benefits and environmental factors, it is recommended to use D2N2 as the appropriate treatment. Full article
(This article belongs to the Special Issue Effects of Salt Stress on Crop Production)
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<p>Plant height of alfalfa at different planting densities and nitrogen applications such as D1N1, D1N2, D1N3, D2N1, D2N2, D2N3, D3N1, D3N1, D3N2, and D3N3 at 60, 90, 120, and 150 DAS. Different letters indicate significant differences between different treatments at same growth stage at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>Leaf area of alfalfa at different planting densities and nitrogen applications such as D1N1, D1N2, D1N3, D2N1, D2N2, D2N3, D3N1, D3N1, D3N2, and D3N3 at 60, 90, 120, and 150 DAS. Different letters indicate significant differences between different treatments at same growth stage at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>Fresh weight of alfalfa at different planting densities and nitrogen applications such as D1N1, D1N2, D1N3, D2N1, D2N2, D2N3, D3N1, D3N1, D3N2, and D3N3 at 60, 90, 120, and 150 DAS. Different letters indicate significant differences between different treatments at same growth stage at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>Dry weight of alfalfa at different planting densities and nitrogen applications such as D1N1, D1N2, D1N3, D2N1, D2N2, D2N3, D3N1, D3N1, D3N2, and D3N3 at 60, 90, 120, and 150 DAS. Different letters indicate significant differences between different treatments at same growth stage at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>Effect of chlorophyll a in alfalfa at different planting densities and nitrogen applications such as D1N1, D1N2, D1N3, D2N1, D2N2, D2N3, D3N1, D3N1, D3N2, and D3N3 at 60, 90, 120, and 150 DAS. Different letters indicate significant differences between different treatments at same growth stage at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>Effect of chlorophyll b in alfalfa at different planting densities and nitrogen applications such as D1N1, D1N2, D1N3, D2N1, D2N2, D2N3, D3N1, D3N1, D3N2, and D3N3 at 60, 90, 120, and 150 DAS. Different letters indicate significant differences between different treatments at same growth stage at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>Effect of carotenoids in alfalfa at different planting densities and nitrogen applicationssuch as D1N1, D1N2, D1N3, D2N1, D2N2, D2N3, D3N1, D3N1, D3N2, and D3N3 at 60, 90, 120, and 150 DAS. Different letters indicate significant differences between different treatments at same growth stage at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>Sucrose content of alfalfa at different planting densities and nitrogen application at such as D1N1, D1N2, D1N3, D2N1, D2N2, D2N3, D3N1, D3N1, D3N2, and D3N3 at 60, 90, 120, and 150 DAS. Different letters indicate significant differences between different treatments at same growth stage at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>Starch content of alfalfa at different planting densities and nitrogen applications such as D1N1, D1N2, D1N3, D2N1, D2N2, D2N3, D3N1, D3N1, D3N2, and D3N3 at 60, 90, 120, and 150 DAS. Different letters indicate significant differences between different treatments at same growth stage at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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13 pages, 5948 KiB  
Article
Water Spinach (Ipomoea aquatica F.) Effectively Absorbs and Accumulates Microplastics at the Micron Level—A Study of the Co-Exposure to Microplastics with Varying Particle Sizes
by Yachuan Zhao, Can Hu, Xufeng Wang, Hui Cheng, Jianfei Xing, Yueshan Li, Long Wang, Tida Ge, Ao Du and Zaibin Wang
Agriculture 2024, 14(2), 301; https://doi.org/10.3390/agriculture14020301 - 13 Feb 2024
Cited by 1 | Viewed by 3143
Abstract
The absorption of microplastics (MPs; size < 5 mm) by plants has garnered increasing global attention owing to its potential implications for food safety. However, the extent to which leafy vegetables can absorb large amounts of MPs, particularly those > 1 μm, remains [...] Read more.
The absorption of microplastics (MPs; size < 5 mm) by plants has garnered increasing global attention owing to its potential implications for food safety. However, the extent to which leafy vegetables can absorb large amounts of MPs, particularly those > 1 μm, remains insufficiently demonstrated. To address this gap in knowledge, we conducted water culture experiments using water spinach (Ipomoea aquatica F.) as a model plant. The roots of water spinach were exposed to a mixed solution that contained fluorescently labeled polystyrene (PS) beads with particle sizes of 200 nm and 1 μm for 10 d. We utilized laser confocal scanning microscopy and scanning electron microscopy to record the absorption, migration, and patterns of accumulation of these large particle sizes of MPs within water spinach. Our findings revealed that micron-sized PS beads were absorbed by the roots in the presence of submicron PS beads and subsequently transported through the exosomes to accumulate to significant levels in the leaves. Short-term hydroponic experiments further indicated that high concentrations of PS bead solutions significantly inhibited the growth of water spinach owing to their large specific surface area that hindered the uptake of water and nutrients by the roots. In conclusion, both sizes of PS beads were found to be absorbed by water spinach, thereby increasing the risk associated with direct human consumption of microplastics in fruits and vegetables. This study provides valuable scientific insights to assess the pollution risks related to fruits and vegetables, as well as ensuring vegetable safety. Full article
(This article belongs to the Section Crop Production)
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<p>Uptake of microplastics and absorption process. (<b>a</b>) Laser scanning confocal microscopic images of the root and stem tissues of water spinach grown for 10 days under a polystyrene (PS)—Microplastics (MPs) concentration of 0 mg/L and 50 mg/L. (<b>b</b>) The process of MPs absorption by roots. Scale bar: 100 μm.</p>
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<p>(<b>a</b>) Laser scanning confocal microscopic images of the leaf tissues of water spinach grown for 10 days following exposure to a mixture of 200 nm and 1 μm PS-MPs. (<b>b</b>) Crystal structures in the cells of the leaf vascular tissue; SEM images of the interstitial cell walls of the xylem of the primary root after 10 days of exposure to a mixture of 200 nm and 1 μm PS-MPs. (<b>c</b>) Schematic diagram of passive uptake process of PS-MPs in water spinach. Scale bar: 400 μm.</p>
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<p>Scanning electron microscopic images of transverse sections of fluorescence-labeled vascular tissue of water spinach roots after 10-day treatment with PS-MPs (200 nm, 1 μm, 0 mg/L, and 50 mg/L). (<b>a</b>,<b>b</b>) show the overall views of the root stele and the xylem vascular bundles of water spinach. (<b>c</b>,<b>e</b>) and (<b>d</b>,<b>f</b>) show enlarged views of the red square areas in (<b>a</b>) and (<b>b</b>), respectively.</p>
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<p>Scanning electron microscopic images of transverse sections of vascular tissues of water spinach stems after 10-day treatment with PS-MPs (200 nm, 1 μm, 0 mg/L, and 50 mg/L). (<b>a</b>,<b>b</b>) show the overall views of the vascular bundles in the stem of an empty leaf, while (<b>c</b>,<b>e</b>) and (<b>d</b>,<b>f</b>) show enlarged views of the red rectangular areas in (<b>a</b>) and (<b>b</b>), respectively. The red arrows in (<b>d</b>) point to 200 nm and 1 μm PS-MPs, while the red arrows in (<b>f</b>) point to 200 nm PS-MPs.</p>
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<p>Scanning electron microscopic images of transverse sections of vascular tissues of water spinach leaves (including the veins) after a 10-day treatment with PS-MPs (200 nm, 1 μm, 0 mg/L, and 50 mg/L). (<b>a</b>,<b>b</b>) show the overall appearance of vascular bundles in the cross-section of the leaf blade. (<b>c</b>,<b>d</b>) show enlarged views of the red boxes in (<b>a</b>) and (<b>b</b>), respectively.</p>
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<p>Transmission electron microscopic images of cross-sections of the roots and leaves of water spinach grown without added PS-MPs. The numbers in the top right corner of panels (<b>a</b>–<b>f</b>) represent the magnification factors, and the red arrows point to micro-fractures between cell walls.</p>
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21 pages, 14367 KiB  
Article
Seeds of Cross-Sector Collaboration: A Multi-Agent Evolutionary Game Theoretical Framework Illustrated by the Breeding of Salt-Tolerant Rice
by Yusheng Chen, Zhaofa Sun, Yanmei Wang, Ye Ma and Weili Yang
Agriculture 2024, 14(2), 300; https://doi.org/10.3390/agriculture14020300 - 13 Feb 2024
Cited by 1 | Viewed by 1434
Abstract
In the context of global food security and the pursuit of sustainable agricultural development, fostering synergistic innovation in the seed industry is of strategic importance. However, the collaborative innovation process between seed companies, research institutions, and governments is fraught with challenges due to [...] Read more.
In the context of global food security and the pursuit of sustainable agricultural development, fostering synergistic innovation in the seed industry is of strategic importance. However, the collaborative innovation process between seed companies, research institutions, and governments is fraught with challenges due to information asymmetry and bounded rationality within the research and development phase. This paper establishes a multi-agent evolutionary game framework, taking the breeding of salt-tolerant rice as a case study. This study, grounded in the theories of information asymmetry and bounded rationality, constructs a two-party evolutionary game model for the interaction between enterprises and research institutions under market mechanisms. It further extends this model to include government participation, forming a three-party evolutionary game model. The aim is to uncover the evolutionary trends in collaborative behavior under various policy interventions and to understand how governments can foster collaborative innovation in salt-tolerant rice breeding through policy measures. To integrate the impact of historical decisions on the evolution of collaborative innovation, this research employs a delay differential equation (DDE) algorithm that takes historical lags into account within the numerical simulation. The stability analysis and numerical simulation using the DDE algorithm reveal the risk of market failure within the collaborative innovation system for salt-tolerant rice breeding operating under market mechanisms. Government involvement can mitigate this risk by adjusting incentive and restraint mechanisms to promote the system’s stability and efficiency. Simulation results further identify that the initial willingness to participate, the coefficient for the distribution of benefits, the coefficient for cost sharing, and the government’s punitive and incentivizing intensities are crucial factors affecting the stability of collaborative innovation. Based on these findings, the study suggests a series of policy recommendations including enhancing the initial motivation for participation in collaborative innovation, refining mechanisms for benefit distribution and cost sharing, strengthening regulatory compliance systems, constructing incentive frameworks, and encouraging information sharing and technology exchange. These strategies aim to establish a healthy and effective ecosystem for collaborative innovation in salt-tolerant rice breeding. While this research uses salt-tolerant rice breeding as a case study, the proposed cooperative mechanisms and policy suggestions have universal applicability in various agricultural science and technology innovation scenarios, especially when research meets widespread social needs but lacks commercial profit drivers, underscoring the essential role of government incentives and support. Consequently, this research not only contributes a new perspective to the application of evolutionary game theory in agricultural science and technology innovation but also offers empirical backing for policymakers in advancing similar collaborative innovation endeavors. Full article
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Graphical abstract

Graphical abstract
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<p>Phase Diagrams of the Two-Party Dynamic Evolutionary Game System.</p>
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<p>Phase Diagram of Three-Party Game Dynamic System Evolution in Scenario 1.</p>
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<p>Phase Diagram of Three-Party Game Dynamic System Evolution in Scenario 2.</p>
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<p>Sensitivity Analysis of Initial Participation Willingness.</p>
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<p>Sensitivity Analysis of Benefit Distribution and Cost Sharing.</p>
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<p>Sensitivity Analysis of Government Penalty Intensity.</p>
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<p>Sensitivity Analysis of Government Subsidy Intensity.</p>
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