Search Results (1,614)

Early blight and ladybug beetle infestation are important factors threatening potato yields. The current research on disease classification using the spectral differences between the healthy and disease-stressed leaves of plants has achieved good progress in a variety of crops, but less research has been conducted on early blight in potato. This paper proposes a CARS-SPA-GA feature selection method. First, the raw spectral data of potato leaves in the visible/near-infrared light region were preprocessed. Then, the feature wavelengths were selected via competitive adaptive reweighted sampling (CARS) and the successive projection algorithm (SPA), respectively. Then, the two sets of wavelengths were reorganized and duplicates were removed, and secondary feature selection was conducted with genetic algorithm (GA). Finally, the feature wavelengths were fed into different classifiers and the parameters were optimized using a real-coded genetic algorithm (RCGA). The experimental results show that the feature wavelengths selected by the CARS-SPA-GA method accounted only for 9% of the full band, and the classification accuracy of the RCGA-optimized support vector machine (SVM) classification model reached 98.366%. These results show that it is feasible to classify early blight and ladybug beetle infestation in potato using visible/near-infrared spectral data, and the CARS-SPA-GA method can substantially improve the accuracy and detection efficiency of potato pest and disease classification. Full article

Corn is one of the global staple grains and the largest grain crop in China. During harvesting, grain separation, and corn production, corn is susceptible to mechanical damage including surface cracks, internal cracks, and breakage. However, the internal cracks are difficult to observe. In this study, hyperspectral imaging was used to detect mechanical damage in corn seeds. The corn seeds were divided into four categories that included intact, broken, internally cracked, and surface-crackedtv. This study compared three feature extraction methods, including principal component analysis (PCA), kernel PCA (KPCA), and factor analysis (FA), as well as a joint feature extraction method consisting of a combination of these methods. The dimensionality reduction results of the three methods (FA + KPCA, KPCA + FA, and PCA + FA) were combined to form a new combined dataset and improve the classification. We then compared the effects of six classification models (ResNet, ShuffleNet-V2, MobileNet-V3, ResNeSt, EfficientNet-V2, and MobileNet-V4) and proposed a ResNeSt_E network based on the ResNeSt and efficient multi-scale attention modules. The accuracy of ResNeSt_E reached 99.0%, and this was 0.4% higher than that of EfficientNet-V2 and 0.7% higher than that of ResNeSt. Additionally, the number of parameters and memory requirements were reduced and the frames per second were improved. We compared two dimensionality reduction methods: KPCA + FA and PCA + FA. The classification accuracies of the two methods were the same; however, PCA + FA was much more efficient than KPCA + FA and was more suitable for practical detection. The ResNeSt_E network could detect both internal and surface cracks in corn seeds, making it suitable for mobile terminal applications. The results demonstrated that detecting mechanical damage in corn seeds using hyperspectral images was possible. This study provides a reference for mechanical damage detection methods for corn. Full article

This study aims to enhance maize water demand calculation. We calculate crop evapotranspiration (ETc) through mobile phone photography and meteorological parameters. In terms of crop coefficient (Kc) calculation, we utilize the mobile phone camera image driver to establish a real-time monitoring model of Kc based on plant canopy coverage (PGC) changes. The calculation of PGC is achieved by constructing a PGC classification network and a Convolutional Block Attention Module (CBAM)-U²Net is implemented by the segment network. For the reference crop evapotranspiration (ETo) calculation, we constructed a simplified ETo estimation model based on SVR, LSTM, Optuna LSTM, and GWO-SVM using a public meteorological data-driven program, and evaluated its performance. The results demonstrate that our method achieves high classification accuracy for the PGC 98.9% and segmentation accuracy for the CBAM-U²net-based segmentation network 95.68%. The Kc calculation model exhibits a root mean square error (RMSE) of 0.053. In terms of ETo estimation, the Optuna-LSTM model with four variables demonstrates the best estimation effect, with a correlation coefficient (R²) of 0.953. The final R² between the estimated ETc value and the true value is 0.918, with an RMSE of 0.014. This method can effectively estimate the water demand of maize. Full article

Barley (Hordeum vulgare L.) is a significant cereal globally, widely used in human and animal food. Furthermore, it has a strong influence on genotype-by-environment interactions, being considered a highly adaptable crop. This study aimed to estimate the parameters of adaptability and stability for 17 barley genotypes, compared with two controls (BRS 180 and BRS 195) grown under irrigation in the Cerrado. The experiments were conducted from 2017 to 2020, from May to September, in two different experimental areas of Embrapa in the Federal District, Brazil. Five traits were evaluated: 1. Esti mated grain yield (kg ha⁻¹); 2. CL1—commercial classification of first grains (>2.5 mm) (%); 3. TGW—1000-grain weight (g); 4. plant height (cm); 5. cycle—days after emergence to earing (days). The data obtained were analyzed for normality and homogeneity of variance, subjected to individual and joint analysis of variance, with means compared by Tukey’s test at 5% significance and the adaptability and stability parameters estimated for the genotypes. The coefficients of environmental variation (CV%) were generally low, indicating good experimental precision. The PFC 2006053 and PFC 2006054 genotypes have broad adaptability and high stability for most traits and outperformed the controls and the overall experiment average. Full article

China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China. Full article

Modern agricultural research, including fruit tree rootstock evaluations, began in England. In the mid-1800s, field plots were established at the Rothamsted Research Station to evaluate cultivars and fertilizer treatments for annual crops. By the early 1900s, farmers questioned the value of field experimentation because the results were not always valid due to inadequate randomization and replication and poor data summarization. During the first half of the 20th century, Rothamsted statisticians transformed field plot experimentation. Field trials were tremendously improved by incorporating new experimental concepts, such as randomization rather than systematically arranging treatments, the factorial arrangement of treatments to simultaneously test multiple hypotheses, and consideration of experimental error. Following the classification of clonal apple rootstocks at the East Malling Research Station in the 1920s, the first rootstock trials were established to compare rootstocks and evaluate rootstock performance on different soil types and with different scion cultivars. Although most of the statistical methods were developed for annual crops and perennial crops are more variable and difficult to work with, rootstock researchers were early adopters of these concepts because the East Malling staff included both pomologists and statisticians. Many of the new statistical concepts were incorporated into on-farm demonstration plots to promote early farmer adoption of new practices. Recent enhancements in computing power have led to the rapid expansion of statistical theory, the development of new statistical methods, and new statistical programming environments, such as R. Over the past century, in many regions of the world, the adoption of new statistical methods has lagged their development. This review is intended to summarize the adoption of error-controlling experimental designs by rootstock researchers, to describe statistical methods used to summarize the resulting data, and to provide suggestions for designing and analyzing future trials. Full article

Changes in soil fertility have led to a decline in crop production, making it challenging for farmers to select the best crops based on soil conditions. Accurate crop prediction can significantly enhance crop productivity, and machine learning plays a crucial role in this process. Crop forecasting is influenced by soil, geographic, and environmental characteristics, with feature selection being essential for identifying suitable crops. In this study, we developed a real-time soil fertility analyzer to obtain the real-time values of soil parameters such as potassium, phosphorus, nitrogen content, temperature, pH, moisture content, and electrical conductivity. The crops examined were coconut, ginger, plantain, and tapioca. The data collected from this analysis served as the dataset for different training and testing classification algorithms for crop prediction using 100 soil samples. Among the algorithms tested, the k-nearest neighbors (KNN) algorithm demonstrated the highest performance, with an accuracy of 84%, precision of 85%, recall of 88.8%, and specificity of 92.4%. These results indicate that machine learning, combined with real-time soil analysis, can effectively predict suitable crops, enhancing crop productivity and aiding farmers in making informed decisions. This approach can revolutionize traditional farming practices by providing precise, data-driven insights into crop selection, ultimately improving agricultural efficiency and sustainability. Full article

As the primary grain crop in China, wheat holds a significant position in the country’s agricultural production, circulation, consumption, and various other aspects. However, the presence of imperfect grains has greatly impacted wheat quality and, subsequently, food security. In order to detect perfect wheat grains and six types of imperfect grains, a method for the fast and non-destructive identification of imperfect wheat grains using hyperspectral images was proposed. The main contents and results are as follows: (1) We collected wheat grain hyperspectral data. Seven types of wheat grain samples, each containing 300 grains, were prepared to construct a hyperspectral imaging system for imperfect wheat grains, and visible near-infrared hyperspectral data from 2100 wheat grains were collected. The Savitzky–Golay algorithm was used to analyze the hyperspectral images of wheat grains, selecting 261 dimensional effective hyperspectral datapoints within the range of 420.61–980.43 nm. (2) The Successive Projections Algorithm was used to reduce the dimensions of the 261 dimensional hyperspectral datapoints, selecting 33 dimensional hyperspectral datapoints. Principal Component Analysis was used to extract the optimal spectral wavelengths, specifically selecting hyperspectral images at 647.57 nm, 591.78 nm, and 568.36 nm to establish the dataset. (3) Particle Swarm Optimization was used to optimize the Support Vector Machines model, Convolutional Neural Network model, and MobileNet V2 model, which were established to recognize seven types of wheat grains. The comprehensive recognition rates were 93.71%, 95.14%, and 97.71%, respectively. The results indicate that a larger model with more parameters may not necessarily yield better performance. The research shows that the MobileNet V2 network model exhibits superior recognition efficiency, and the integration of hyperspectral image technology with the classification model can accurately identify imperfect wheat grains. Full article

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product. Full article

Low-intensity, diversified agricultural land use is needed to counteract the current decline in agrobiodiversity. Landscape ecology tools can support agrobiodiversity assessment efforts by investigating biodiversity-related ecological functions (pattern–process paradigm). In this study, we test a toolkit of landscape ecology analyses to compare different farm management models: polyculture agroforestry (POLY) vs. conventional monoculture crop management (CV). Farm-scale analyses are applied on temperate alluvial sites (Po Plain, Northern Italy), as part of a broader multi-scale analytical approach. We analyze the landscape ecological quality through landscape matrix composition, patch shape complexity, diversity, metastability, and connectivity indices. We assess farm differences through multivariate analyses and t-tests and test a farm classification tool, namely, a scoring system based on the relative contributions of POLY farms, considering their deviation from a local CV baseline. The results showed a separate ecological behavior of the two models. The POLY model showed better performance, with significant positive contributions to the forest and semi-natural component equipment and diversity; agricultural component diversity, metastability; total farm diversity, metastability, connectivity, and circuitry. A reference matrix for the ecological interpretation of the results is provided. Farm classification provides a quick synthesis of such contributions, facilitating farm comparisons. The methodology has a low cost and quickly provides information on ongoing ecological processes resulting from specific farm management practices; it is intended to complement field-scale assessments and could help to meet the need for a partially outcome-based assessment of good farm practice. Full article

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications. Full article

The genus Dickeya consists of Gram-negative bacteria capable of causing soft rot symptoms in plants, which involves tissue breakdown, particularly in storage organs such as tubers, rhizomes, and bulbs. These bacteria are ranked among the top ten most relevant phytopathogens and seriously threaten economically valuable crops and ornamental plants. This study employs a genomic analysis approach to taxonomically classify and characterize the resistome and virulome of two new strains, CCRMP144 and CCRMP250, identified as Dickeya dadantii. These strains were found to be the causative agents of soft rot symptoms in chili pepper (Capsicum spp.) and lettuce (Lactuca sativa), respectively, in the northeastern region of Brazil. The methodology employed in silico techniques, including tetra correlation search (TCS) and Average Nucleotide Identity (ANI) analysis, in association with a phylogenomic tree inference. TCS and ANI analysis showed that the studied strains belong to the Dickeya dadantii species. The phylogenomic analysis grouped the studied strains in the D. dadantii clade. The genomic characterization demonstrates 68 virulence genes, 54 resistances of biocide and heavy metal genes, and 23 antibiotic resistance genes. As far as we know, this is the first genomic study with Brazilian D. dadantii strains. This study demonstrates the efficacy to taxonomic classification and provides insights into the pathogenesis, host range, and adaptability of these strains which are crucial for the development of more effective management and control strategies for soft rot diseases. Full article

Weeds have a detrimental effect on crop yield. However, the prevailing chemical weed control methods cause pollution of the ecosystem and land. Therefore, it has become a trend to reduce dependence on herbicides; realize a sustainable, intelligent weed control method; and protect the land. In order to realize intelligent weeding, efficient and accurate crop and weed recognition is necessary. Convolutional neural networks (CNNs) are widely applied for weed and crop recognition due to their high speed and efficiency. In this paper, a multi-path input skip-residual network (SkipResNet) was put forward to upgrade the classification function of weeds and crops. It improved the residual block in the ResNet model and combined three different path selection algorithms. Experiments showed that on the plant seedling dataset, our proposed network achieved an accuracy of 95.07%, which is 0.73%, 0.37%, and 4.75% better than that of ResNet18, VGG19, and MobileNetV2, respectively. The validation results on the weed–corn dataset also showed that the algorithm can provide more accurate identification of weeds and crops, thereby reducing land contamination during the weeding process. In addition, the algorithm is generalizable and can be used in image classification in agriculture and other fields. Full article

Tomato diseases, including Leaf blight, Leaf curl, Septoria leaf spot, and Verticillium wilt, are responsible for up to 50% of annual yield loss, significantly impacting global tomato production, valued at approximately USD 87 billion. In Ghana, there is a yield gap of about 50% in tomato production, which requires drastic measures to increase the yield of tomatoes. Conventional diagnostic methods are labor-intensive and impractical for real-time application, highlighting the need for innovative solutions. This study addresses these issues in Ghana by utilizing Edge Impulse to deploy multiple deep-learning models on a single mobile device, facilitating the rapid and precise detection of tomato leaf diseases in the field. This work compiled and rigorously prepared a comprehensive Ghanaian dataset of tomato leaf images, applying advanced preprocessing and augmentation techniques to enhance robustness. Using TensorFlow, we designed and optimized efficient convolutional neural network (CNN) architectures, including MobileNet, Inception, ShuffleNet, Squeezenet, EfficientNet, and a custom Deep Neural Network (DNN). The models were converted to TensorFlow Lite format and quantized to int8, substantially reducing the model size and improving inference speed. Deployment files were generated, and the Edge Impulse platform was configured to enable multiple model deployments on a mobile device. Performance evaluations across edge hardware provided metrics such as inference speed, accuracy, and resource utilization, demonstrating reliable real-time detection. EfficientNet achieved a high training accuracy of 97.12% with a compact 4.60 MB model size, proving its efficacy for mobile device deployment. In contrast, the custom DNN model is optimized for microcontroller unit (MCU) deployment. This edge artificial intelligence (AI) technology integration into agricultural practices offers scalable, cost-effective, and accessible solutions for disease classification, enhancing crop management, and supporting sustainable farming practices. Full article

Background: Thymoma is a tumor that originates in the thymus gland, a part of the human body located behind the breastbone. It is a malignant disease that is rare in children but more common in adults and usually does not spread outside the thymus. The exact cause of thymic disease is not known, but it is thought to be more common in people infected with the EBV virus at an early age. Various surgical methods are used in clinical settings to treat thymoma. Expert opinion is very important in the diagnosis of the disease. Recently, next-generation technologies have become increasingly important in disease detection. Today’s early detection systems already use transformer models that are open to technological advances. Methods: What makes this study different is the use of transformer models instead of traditional deep learning models. The data used in this study were obtained from patients undergoing treatment at Fırat University, Department of Thoracic Surgery. The dataset consisted of two types of classes: thymoma disease images and non-thymoma disease images. The proposed approach consists of preprocessing, model training, feature extraction, feature set fusion between models, efficient feature selection, and classification. In the preprocessing step, unnecessary regions of the images were cropped, and the region of interest (ROI) technique was applied. Four types of transformer models (Deit3, Maxvit, Swin, and ViT) were used for model training. As a result of the training of the models, the feature sets obtained from the best three models were merged between the models (Deit3 and Swin, Deit3 and ViT, Deit3 and ViT, Swin and ViT, and Deit3 and Swin and ViT). The combined feature set of the model (Deit3 and ViT) that gave the best performance with fewer features was analyzed using the mRMR feature selection method. The SVM method was used in the classification process. Results: With the mRMR feature selection method, 100% overall accuracy was achieved with feature sets containing fewer features. The cross-validation technique was used to verify the overall accuracy of the proposed approach and 99.22% overall accuracy was achieved in the analysis with this technique. Conclusions: These findings emphasize the added value of the proposed approach in the detection of thymoma. Full article