Search Results (2,353)

The accumulation of cadmium (Cd) in agricultural soils presents a significant threat to crop safety, emphasizing the critical necessity for effective monitoring and management of soil Cd levels. Despite technological advancements, accurately monitoring soil Cd concentrations using satellite hyperspectral technology remains challenging, particularly in efficiently extracting spectral information. In this study, a total of 304 soil samples were collected from agricultural soils surrounding a tungsten mine located in the Xiancha River basin, Jiangxi Province, Southern China. Leveraging hyperspectral data from the ZY1-02D satellite, this research developed a comprehensive framework that evaluates the predictive accuracy of nine spectral transformations across four modeling approaches to estimate soil Cd concentrations. The spectral transformation methods included four logarithmic and reciprocal transformations, two derivative transformations, and three baseline correction and normalization transformations. The four models utilized for predicting soil Cd were partial least squares regression (PLSR), support vector machine (SVM), bidirectional recurrent neural networks (BRNN), and random forest (RF). The results indicated that these spectral transformations markedly enhanced the absorption and reflection features of the spectral curves, accentuating key peaks and troughs. Compared to the original spectral curves, the correlation analysis between the transformed spectra and soil Cd content showed a notable improvement, particularly with derivative transformations. The combination of the first derivative (FD) transformation with the RF model yielded the highest accuracy (R² = 0.61, RMSE = 0.37 mg/kg, MAE = 0.21 mg/kg). Furthermore, the RF model in multiple spectral transformations exhibited higher suitability for modeling soil Cd content compared to other models. Overall, this research highlights the substantial applicative potential of the ZY1-02D satellite hyperspectral data for detecting soil heavy metals and provides a framework that integrates optimal spectral transformations and modeling techniques to estimate soil Cd contents. Full article

Maize is the world’s largest food crop and plays a critical role in global food security. Accurate phenology information is essential for improving yield estimation and enabling timely field management. Yet, much of the research has concentrated on general crop growth periods rather than on pinpointing key phenological stages. This gap in understanding presents a challenge in determining how different vegetation indices (VIs) might accurately extract phenological information across these stages. To address this, we employed the shape model fitting (SMF) method to assess whether a multi-index approach could enhance the precision of identifying key phenological stages. By analyzing time-series data from various VIs, we identified five phenological stages (emergence, seven-leaf, jointing, flowering, and maturity stages) in maize cultivated in Jilin Province. The findings revealed that each VI had distinct advantages depending on the phenological stage, with the land surface water index (LSWI) being particularly effective for jointing and flowering stages due to its correlation with vegetation water content, achieving a root mean square error (RMSE) of three to four days. In contrast, the normalized difference vegetation index (NDVI) was more effective for identifying the emergence and seven-leaf stages, with an RMSE of four days. Overall, combining multiple VIs significantly improved the accuracy of phenological stage identification. This approach offers a novel perspective for utilizing diverse VIs in crop phenology, thereby enhancing the precision of agricultural monitoring and management practices. Full article

Yam (Dioscorea spp.) is a main staple tuber crop in Nigeria and the West African region. Its performance is determined by genotypes and also the environment of growth. This study assessed the agronomic performance and yield stability of elite white yam (Dioscorea rotundata) genotypes across diverse Nigerian environments. A total of 25 genotypes were evaluated at three locations in two consecutive growing seasons, 2022 and 2023, for fresh tuber yield, disease resistance, and tuber quality traits. The genotype’s performance and stability for the measured traits were assessed using various analytical tools such as additive main effects and multiplicative interaction (AMMI) and multi-trait stability index (MTSI). The AMMI analysis revealed significant differences among the genotypes and across the environments for all traits (p < 0.001, p < 0.01). The PCA revealed that the first two principal components (PC1 and PC2) explained a substantial portion of the total variation (49.84%). The MTSI identified four clones: G18, G19, G24, and G16 as promising candidates for improved yam production in Nigeria with high and stable performance for the multiple traits. Full article

Plant height is a crucial indicator of crop growth. Rapid measurement of crop height facilitates the implementation and management of planting strategies, ensuring optimal crop production quality and yield. This paper presents a low-cost method for the rapid measurement of multiple lettuce heights, developed using an improved YOLOv8n-seg model and the stacking characteristics of planes in depth images. First, we designed a lightweight instance segmentation model based on YOLOv8n-seg by enhancing the model architecture and reconstructing the channel dimension distribution. This model was trained on a small-sample dataset augmented through random transformations. Secondly, we proposed a method to detect and segment the horizontal plane. This method leverages the stacking characteristics of the plane, as identified in the depth image histogram from an overhead perspective, allowing for the identification of planes parallel to the camera’s imaging plane. Subsequently, we evaluated the distance between each plane and the centers of the lettuce contours to select the cultivation substrate plane as the reference for lettuce bottom height. Finally, the height of multiple lettuce plants was determined by calculating the height difference between the top and bottom of each plant. The experimental results demonstrated that the improved model achieved a 25.56% increase in processing speed, along with a 2.4% enhancement in mean average precision compared to the original YOLOv8n-seg model. The average accuracy of the plant height measurement algorithm reached 94.339% in hydroponics and 91.22% in pot cultivation scenarios, with absolute errors of 7.39 mm and 9.23 mm, similar to the sensor’s depth direction error. With images downsampled by a factor of 1/8, the highest processing speed recorded was 6.99 frames per second (fps), enabling the system to process an average of 174 lettuce targets per second. The experimental results confirmed that the proposed method exhibits promising accuracy, efficiency, and robustness. Full article

Updated and accurate crop yield maps play a key role in the agricultural environment. Their application enables the support for sustainable agricultural practices and the formulation of effective strategies to mitigate the impacts of climate change. Farmers can apply the maps to gain an overview of the yield variability, improving farm management practices and optimizing inputs to increase productivity and sustainability such as fertilizers. Earth observation (EO) data make it possible to map crop yield estimations over large areas, although this will remain challenging for specific crops such as sugarcane. Yield data collection is an expensive and time-consuming practice that often limits the number of samples collected. In this study, the sugarcane yield estimation based on a small number of training datasets within smallholder crop systems in the Tha Khan Tho District, Thailand for the year 2022 was assessed. Specifically, multi-temporal satellite datasets from multiple sensors, including Sentinel-2 and Landsat 8/9, were involved. Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). Among these algorithms, the RFR model demonstrated outstanding performance, yielding an excellent result compared to existing techniques, achieving an R-squared (R²) value of 0.79 and a root mean square error (RMSE) of 3.93 t/ha (per 10 m × 10 m pixel). Furthermore, the mapped yields across the region closely aligned with the official statistical data from the Office of the Cane and Sugar Board (with a range value of 36,000 ton). Finally, the sugarcane yield estimation model was applied to over 2100 sugarcane fields in order to provide an overview of the current state of the yield and total production in the area. In this work, the different yield rates at the field level were highlighted, providing a powerful workflow for mapping sugarcane yields across large regions, supporting sugarcane crop management and facilitating decision-making processes. Full article

While atmospheric warming intensifies the global water cycle, regionalised effects of climate change on water loss, irrigation supply, and food security are highly variable. Here, we elucidate the impacts of the climate crisis on irrigation water availability and cropping area in Nepal’s largest irrigation scheme, the Sunsari Morang Irrigation Scheme (SMIS), by accounting for the hydraulic capacity of existing canal systems, and potential changes realised under future climates. To capture variability implicit in climate change projections, we invoke multiple Representative Concentration Pathways (RCPs; 4.5 and 8.5) across three time horizons (2016–2045, 2036–2065, and 2071–2100). We reveal that although climate change increases water availability to agriculture from December through March, the designed discharge of 60 m³/s would not be available in February-March for both RCPs under all three time horizons. Weed growth, silt deposition, and poor maintenance have reduced the current canal capacity from the design capacity of 60 m³/s to 53 m³/s up to 10.7 km from the canal intake (representing a 12% reduction in the discharge capacity of the canal). Canal flow is further reduced to 35 m³/s at 13.8 km from canal intake, representing a 27% reduction in flow capacity relative to the original design standards. Based on climate projections, and assuming ceteris paribus irrigation infrastructure, total wheat cropping area could increase by 12–19%, 23–27%, and 12–35% by 2016–2045, 2036–2065, and 2071–2100, respectively, due to increased water availability borne by the changing climate. The case for further investment in irrigation infrastructure via water diversion, or installation of efficient pumps at irrigation canal intakes is compelling. Such investment would catalyse a step-change in the agricultural economy that is urgently needed to sustain the Nepalese economy, and thus evoke beneficial cascading implications for global food security. Full article

Potatoes (Solanum tuberosum L.) are an important plant crop, whose yield may vary significantly depending on pedo-climatic conditions and genotype. Therefore, the analysis of the genotype × environment interaction (GEI) is mandatory for the setup of high-yielding and stable potato genotypes. This research evaluated the tuber yield (t ha⁻¹) and yield characteristic of nine potato cultivars over 3 years and 4 organic farms in Poland by additive main effects and multiplicative interactions (AMMIs) and genotype plus genotype environment interaction (GGE) biplot analyses. The results of these analyses indicated significant differentiation of tuber yield among genotypes in individual environments. It was found that the environment (E, where E = L (localization) × Y (year)), genotype (G) and GEI, but not replication, significantly affected tuber yield. The AMMI analysis showed that the environment factor explained the most considerable part of tuber yield variations (52.3%), while the GEI and G factors explained a much lower part of the variations. The AMMI and GGE analyses identified five cvs.: Twister (46.4 t ha⁻¹), Alouette (35.8 t ha⁻¹), Kokra (34.8 t ha⁻¹), Levante (33.1 t ha⁻¹), and Gardena (30.4 t ha⁻¹), as leading cultivars in the studied organic farms due to their high productivity coupled with yield stability. The statistical measure Kang (YS_i) showed that these cvs. can be considered as adaptable to a wide range of organic environments. In the case of morphological traits of tubers (tuber shape and depth of tuber eyes), the most important factor influencing both these traits was genotype (G). Influence of other factors, like localization (L), year (Y), and all interactions (double and triple), were much less significant or insignificant. In case of taste and non-darkening of tuber flesh, the main effects which significantly affected the values of these traits were genotype (G) and localization (L). We observed that cooking type can vary depending on the year (Y) and the localization (L). Full article

Alternative oxidase (AOX) serves as a critical terminal oxidase within the plant respiratory pathway, playing a significant role in cellular responses to various stresses. Foxtail millet (Setaria italica), a crop extensively cultivated across Asia, is renowned for its remarkable tolerance to abiotic stresses and minimal requirement for fertilizer. In this study, we conducted a comprehensive genome-wide identification of AOX genes in foxtail millet genome, discovering a total of five SiAOX genes. Phylogenetic analysis categorized these SiAOX members into two subgroups. Prediction of cis-elements within the promoter regions, coupled with co-expression network analysis, intimated that SiAOX proteins are likely involved in the plant’s adaptive response to abiotic stresses. Employing RNA sequencing (RNA-seq) and real-time quantitative PCR (RT-qPCR), we scrutinized the expression patterns of the SiAOX genes across a variety of tissues and under multiple abiotic stress conditions. Specifically, our analysis uncovered that SiAOX1, SiAOX2, SiAOX4, and SiAOX5 display distinct tissue-specific expression profiles. Furthermore, SiAOX2, SiAOX3, SiAOX4, and SiAOX5 exhibit responsive expression patterns under abiotic stress conditions, with significant differences in expression levels observed between the shoot and root tissues of foxtail millet seedlings. Haplotype analysis of SiAOX4 and SiAOX5 revealed that these genes are in linkage disequilibrium, with Hap_2 being the superior haplotype for both, potentially conferring enhanced cold stress tolerance in the cultivar group. These findings suggest that both SiAOX4 and SiAOX5 may be targeted for selection in future breeding programs aimed at improving foxtail millet’s resilience to cold stress. Full article

MicroRNAs (miRNAs) are small, non-coding RNAs that are expressed in a tissue- and temporal-specific manner during development. They have been found to be highly conserved during the evolution of different species. miRNAs regulate the expression of several genes in various organisms, with some regulating the expression of multiple genes with similar or completely unrelated functions. Frequent disease and insect pest infestations severely limit agricultural development. Thus, cultivating resistant crops via miRNA-directed gene regulation in plants, insects, and pathogens is an important aspect of modern breeding practices. To strengthen the application of miRNAs in sustainable agriculture, plant endogenous or exogenous miRNAs have been used for plant breeding. Consequently, the development of biological pesticides based on miRNAs has become an important avenue for future pest control methods. However, selecting the appropriate miRNA according to the desired target traits in the target organism is key to successfully using this technology for pest control. This review summarizes the progress in research on miRNAs in plants and other species involved in regulating plant disease and pest resistance pathways. We also discuss the molecular mechanisms of relevant target genes to provide new ideas for future research on pest and disease resistance and breeding in plants. Full article

As an essential component of the global economic crop, cotton is highly susceptible to the impact of diseases on its yield and quality. In recent years, artificial intelligence technology has been widely used in cotton crop disease recognition, but in complex backgrounds, existing technologies have certain limitations in accuracy and efficiency. To overcome these challenges, this study proposes an innovative cotton disease recognition method called CANnet, and we independently collected and constructed an image dataset containing multiple cotton diseases. Firstly, we introduced the innovatively designed Reception Field Space Channel (RFSC) module to replace traditional convolution kernels. This module combines dynamic receptive field features with traditional convolutional features to effectively utilize spatial channel attention, helping CANnet capture local and global features of images more comprehensively, thereby enhancing the expressive power of features. At the same time, the module also solves the problem of parameter sharing. To further optimize feature extraction and reduce the impact of spatial channel attention redundancy in the RFSC module, we connected a self-designed Precise Coordinate Attention (PCA) module after the RFSC module to achieve redundancy reduction. In the design of the classifier, CANnet abandoned the commonly used MLP in traditional models and instead adopted improved Kolmogorov Arnold Networks-s (KANs) for classification operations. KANs technology helps CANnet to more finely utilize extracted features for classification tasks through learnable activation functions. This is the first application of the KAN concept in crop disease recognition and has achieved excellent results. To comprehensively evaluate the performance of CANnet, we conducted extensive experiments on our cotton disease dataset and a publicly available cotton disease dataset. Numerous experimental results have shown that CANnet outperforms other advanced methods in the accuracy of cotton disease identification. Specifically, on the self-built dataset, the accuracy reached 96.3%; On the public dataset, the accuracy reached 98.6%. These results fully demonstrate the excellent performance of CANnet in cotton disease identification tasks. Full article

Garlic constitutes a significant small-scale agricultural commodity in China. A key factor influencing garlic prices is the planted area, which can be accurately and efficiently determined using remote sensing technology. However, the spectral characteristics of garlic and winter wheat are easily confused, and the widespread intercropping of these crops in the study area exacerbates this issue, leading to significant challenges in remote sensing image analysis. Additionally, remote sensing data are often affected by weather conditions, spatial resolution, and revisit frequency, which can result in delayed and inaccurate area extraction. In this study, historical data were utilized to restore Sentinel-2 remote sensing images, aimed at mitigating cloud and rain interference. Feature combinations were devised, incorporating two vegetation indices into a comprehensive time series, along with Sentinel-1 synthetic aperture radar (SAR) time series and other temporal datasets. Multiple classification combinations were employed to extract garlic within the study area, and the accuracy of the classification results was systematically analyzed. First, we used passive satellite imagery to extract winter crops (garlic, winter wheat, and others) with high accuracy. Second, we identified garlic by applying various combinations of time series features derived from both active and passive remote sensing data. Third, we evaluated the classification outcomes of various feature combinations to generate an optimal garlic cultivation distribution map for each region. Fourth, we developed a garlic fragmentation index to assess the impact of landscape fragmentation on garlic extraction accuracy. The findings reveal that: (1) Better results in garlic extraction can be achieved using active–passive time series remote sensing. The performance of the classification model can be further enhanced by incorporating short-wave infrared bands or spliced time series data into the classification features. (2) Examination of garlic cultivation fragmentation using the garlic fragmentation index aids in elucidating variations in accuracy across the study area’s six counties. (3) Comparative analysis with validation samples demonstrated superior garlic extraction outcomes from the six primary garlic-producing counties of the North China Plain in 2021, achieving an overall precision exceeding 90%. This study offers a practical exploration of target crop identification using multi-source remote sensing data in mixed cropping areas. The methodology presented here demonstrates the potential for efficient, cost-effective, and accurate garlic classification, which is crucial for improving garlic production management and optimizing agricultural practices. Moreover, this approach holds promise for broader applications, such as nationwide garlic mapping. Full article

The transformation of spent coffee grounds (SCGs) into hydrochars has been extensively studied in recent years to explore their potential in biofortifying foods and mitigating the plant toxicity associated with SCGs. This study aimed to evaluate the effects of adding activated (ASCG and AH160) and functionalized SCGs, as well as SCG-derived hydrochars (ASCG-Fe and AH160-Fe), on cucumber production and plant iron content. To achieve this, SCGs and SCG-derived hydrochars activated and functionalized with Fe were incorporated into cucumber crops grown in a greenhouse over multiple harvests. Among the treatments, SCG-Fe proved to be the most promising for cucumber production, yielding an average of 25 kg of cumulative production per treatment across three harvests. Regarding iron content, the average results across all harvests showed that SCGs and functionalized SCG-hydrochars matched the performance of the commercial chelate (0.108 vs. 0.11 mg Fe/100 g fresh weight). However, in subsequent harvests, iron appeared to leach out, with the activated bio-products (ASCG and AH160) leaving the highest iron reserves in the soil. Additionally, the hydrochar activated at 160 °C demonstrated the highest utilization efficiency. In conclusion, the incorporation of SCG residues and second-generation residues (hydrochars) shows promise as agents for biofortifying cucumbers. Full article

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction. Full article

Crop monitoring with unmanned aerial vehicles (UAVs) has the potential to reduce field monitoring costs while increasing monitoring frequency and improving efficiency. However, the utilization of RGB-based UAV imagery for crop-specific monitoring, especially for sugarcane, remains limited. This work proposes a UAV platform with an RGB camera as a low-cost solution to monitor sugarcane fields, complementing the commonly used multi-spectral methods. This new approach optimizes the RGB vegetation indices for accurate prediction of sugarcane growth, providing many improvements in scalable crop-management methods. The images were captured by a DJI Mavic Pro drone. Four RGB vegetation indices (VIs) (GLI, VARI, GRVI, and MGRVI) and the crop surface model plant height (CSM_PH) were derived from the images. The fractional vegetation cover (FVC) values were compared by image classification. Sugarcane plant height predictions were generated using two machine learning (ML) algorithms—multiple linear regression (MLR) and random forest (RF)—which were compared across five predictor combinations (CSM_PH and four VIs). At the early stage, all VIs showed significantly lower values than later stages (p < 0.05), indicating an initial slow progression of crop growth. MGRVI achieved a classification accuracy of over 94% across all growth phases, outperforming traditional indices. Based on the feature rankings, VARI was the least sensitive parameter, showing the lowest correlation (r < 0.5) and mutual information (MI < 0.4). The results showed that the RF and MLR models provided better predictions for plant height. The best estimation results were observed withthe combination of CSM_PH and GLI utilizing RF model (R² = 0.90, RMSE = 0.37 m, MAE = 0.27 m, and AIC = 21.93). This study revealed that VIs and the CSM_PH derived from RGB images captured by UAVs could be useful in monitoring sugarcane growth to boost crop productivity. Full article

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO₂ stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO₂ stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO₂ stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO₂ stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO₂ stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO₂ stress. (2) The 1.2-order derivative dual-band index (RVI (R₇₂₀, R₅₂₂)) constructed by combining the sensitive spectral bands of the CO₂ response of winter wheat leaves achieved a high-precision estimation of the LCC under CO₂ stress conditions (R² = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R² = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO₂ impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R² = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO₂ stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses. Full article