Search Results (164)

The search for higher efficiency in agribusiness supports the adoption of digital tools and Lean Production principles in agricultural spraying, a crucial operation for crops. Spraying is essential to ensure yield, quality, cost efficiency, and environmental protection. This study analyzed operational data from self-propelled sprayers in soybean and corn fields, classifying hours, calculating efficiencies, and applying statistical process control. Efficiencies were investigated by combining Lean Production principles with CAN-based digital monitoring, which enabled the identification of non-value-adding activities and supported the real-time management of spraying operations. The results showed that productive time accounted for 41.2% of total recorded hours, corresponding to effective operation and auxiliary tasks directly associated with the execution of spraying activities. A high proportion of unrecorded hours (21.2%) was also observed, reflecting discrepancies between administrative work schedules and machine-logged data. Additionally, coefficients of variation for operational speed and fuel consumption were 12.1% and 24.0%, respectively. Correcting special causes increased work capacity (4.9%) and reduced fuel consumption (0.9%). Economic simulations, based on efficiencies, operating parameters of the sprayer, and cost indicators, indicated that increasing scale reduces costs when installed capacity is carefully managed. Integrating telemetry with Lean Production principles enables real-time resource optimization and waste reduction. Full article

Automatic cropland monitoring is becoming increasingly important in the advancement of sustainable agriculture. However, multiannual satellite-based crop mapping across different regions remains challenging due to variations in crop phenology and meteorological conditions. The use of multispectral data from a single satellite can also present difficulties in constructing vegetation index time series, particularly in regions affected by persistent cloud cover. In this study, NDVI time series obtained from Sentinel-2 and Landsat-8/9 imagery were fitted using a Fourier series, and daily NDVI composites from the Meteor-M satellite were obtained for Khabarovsk Krai in the Russian Far East from 2022 to 2024. These data were used to perform random forest (RF) classification for each year for five land cover classes: soybean, grain crops, perennial grasses, buckwheat, and fallow land. The average annual classification accuracies were 87% for Landsat 8/9, 89% for Meteor-M, and 93% for Sentinel-2. Combining data from all three satellites improved classification performance, increasing cross-validation overall accuracy from 92% to 96% in 2022, from 96% to 97% in 2023. These results demonstrate the potential of using both individual satellite data for sufficiently accurate mapping and combined datasets, which provide consistently high accuracy and are a reliable alternative when Sentinel data are limited due to cloud cover. Full article

In South Korea, reclaimed coastal tidelands (RTLs) are generally used for rice cultivation rather than upland cultivation; however, there is growing social pressure to change the use of RTLs to upland crop production to increase the self-sufficiency rate regarding grain. However, RTLs are not suitable for cultivating upland crops due to their high salinity, poor drainage, and shallow groundwater levels. Therefore, it is necessary to develop a cost-effective drainage method, such as surface drainage. This study investigated the effects of slope construction on surface drainage and on the growth and yield of soybean (Glycine max (L.) Merr.) in poorly drained fields at the Saemangeum RTL, which is the largest RTL district in South Korea. Slopes were constructed at angles of 0°, 3°, and 5°; soybean was sown in June 2023 (wet season) and May 2024 (dry season); and growth of soybean was monitored at the flowering, pod-filling, and harvest stages. Soil pH, electrical conductivity (EC), and mineral nitrogen (NH₄⁺ and NO₃⁻) were measured monthly, while daily changes in soil water content were measured using soil sensors. As expected, slope construction enhanced surface runoff from the upper to lower slope areas under heavy rainfall, but soil erosion was also increased. Soybean growth and yield were higher in the upper sites for the wet-season conditions mainly due to lowered moisture stress. For the dry-season, there was no significant differences in soybean growth and yield across the slopes due to drought and high temperatures during flowering and pod-filling stages. Soybean growth and yield parameters were negatively correlated with both soil water content and pH. Slope construction improves surface drainage but does not consistently translate into higher soybean yields, highlighting its limited agronomic and economic value when used alone. Instead, integrated management practices combining drainage improvement, supplemental irrigation, and soil erosion reduction need to be implemented to support sustainable upland cropping in coastal RTLs. Full article

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the optimal feature combination (especially newly proposed features) and strategies from the rich feature sets contained in multi-source remote sensing imagery remains one of the challenges. In this paper, we propose a hierarchical feature optimization method, incorporating a newly reported vegetation feature, for mapping crop types by combining the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. The method first calculates spectral features, texture features, polarization features, vegetation index features, and crop phenological features, with a particular focus on infrared band features and the newly developed Kernel Normalized Difference Vegetation Index (kNDVI). These 126 features are then selected to construct 15 crop type mapping models based on different feature combinations and a random forest (RF) classifier. Feature selection was performed using the feature correlation analysis and random forest recursive feature elimination (RF-RFE) to identify the optimal subset. The experiment was conducted in the Linhe region, covering an area of 2333 km². The resulting 10 m crop map, generated by the optimal model (Model 15) with 34 key features, demonstrated that integrating multi-source features significantly enhances mapping accuracy. The model achieved an overall accuracy of 90.10% across five crop types (corn, wheat, sunflower, soybean, and beet), outperforming other representative feature optimization methods, Relief-F (87.50%) and CFS (89.60%). The study underscores the importance of feature optimization and reduction of redundant features while also showcasing the effectiveness of red edge and infrared features, as well as the kNDVI, in mapping crop type. Full article

Electrochemical biosensors have emerged as a promising tool for the early detection of diseases in oilseed crops such as rapeseed, soybean, and peanut. These biosensors offer high sensitivity, portability, and cost-effectiveness. Timely diagnosis is critical, as many pathogens exhibit latent infection phases or produce invisible metabolic toxins, leading to substantial yield losses before visible symptoms occur. This review summarises recent advances in the field of nanomaterial-assisted electrochemical sensing for oilseed crop diseases, with a particular focus on sensor mechanisms, interface engineering, and biomolecular recognition strategies. The following innovations are highlighted: nanostructured electrodes, aptamer- and antibody-based probes, and signal amplification techniques. These innovations have enabled the detection of pathogen DNA, enzymes, and toxins at ultra-low concentrations. Notwithstanding these achievements, challenges persist, including signal interference from plant matrices, limitations in device miniaturization, and the absence of standardized detection protocols. Future research should explore the potential of AI-assisted data interpretation, the use of biodegradable sensor materials, and the integration of these technologies with agricultural IoT networks. The aim of this integration is to enable real-time, field-deployable disease surveillance. The integration of laboratory innovations with field applications has been demonstrated to have significant potential in supporting sustainable agriculture and strengthening food security through intelligent crop health monitoring. Full article

Fire occurs naturally and anthropogenically in the Cerrado biome, influenced by hydrology, climate, topography, and land use. Mapping burned areas is essential for understanding the causes of fire and improving prevention and regulation. However, fire scars are often confused with bare soil in agricultural regions. This study presents a method for mapping burned areas using spectral indices and artificial neural networks (ANN). We evaluated the accuracy of these techniques and identified the best input variables for scar detection. Using Sentinel-2 images from 2018 to 2021 during dry periods, we applied NDVI, SAVI, NBR, and CSI indices. The study included two stages: first, finding optimal classification configurations for fire scars, and second, mapping land use and cover with fire scars and crops. Results showed that using all Sentinel-2 bands and the four indices post-fire achieved over 93.7% accuracy and a kappa index of 0.92. Fire scars were mainly located in areas with temporary crops like soybean, sugarcane, rice, and cotton. This low-cost method allows for effective monitoring of fire scars, underscoring the need to regulate agricultural practices in the Cerrado, where burning poses environmental and health risks. Full article

Accurate and timely estimation of soybean emergence at the plot scale using unmanned aerial vehicle (UAV) remote sensing imagery is essential for germplasm evaluation in breeding programs, where breeders prioritize overall plot-scale emergence rates over subimage-based counts. This study proposes PlotCounter, a deep learning regression model based on the TasselNetV2++ architecture, designed for plot-scale soybean seedling counting. It employs a patch-based training strategy combined with full-plot validation to achieve reliable performance with limited breeding plot data. To incorporate additional agronomic information, PlotCounter is extended into a multitask learning framework (MTL-PlotCounter) that integrates sowing metadata such as variety, number of seeds per hole, and sowing density as auxiliary classification tasks. RGB images of 54 breeding plots were captured in 2023 using a DJI Mavic 2 Pro UAV and processed into an orthomosaic for model development and evaluation, showing effective performance. PlotCounter achieves a root mean square error (RMSE) of 6.98 and a relative RMSE (rRMSE) of 6.93%. The variety-integrated MTL-PlotCounter, V-MTL-PlotCounter, performs the best, with relative reductions of 8.74% in RMSE and 3.03% in rRMSE compared to PlotCounter, and outperforms representative YOLO-based models. Additionally, both PlotCounter and V-MTL-PlotCounter are deployed on a web-based platform, enabling users to upload images via an interactive interface, automatically count seedlings, and analyze plot-scale emergence, powered by a multimodal large language model. This study highlights the potential of integrating UAV remote sensing, agronomic metadata, specialized deep learning models, and multimodal large language models for advanced crop monitoring. Full article

Tillage is a major factor influencing soil biological communities, particularly earthworms, which play a key role in soil structure and nutrient cycling. To address soil degradation, less-intensive tillage practices are increasingly being adopted globally and have shown positive effects on earthworm populations when applied consistently over extended periods. However, understanding of the earthworm population dynamics in the period following the implementation of changes in tillage practices remains limited. This three-year field study (2021–2023) investigates earthworm populations during the early transition phase (4–6 years) following the conversion from conventional ploughing to conservation (<8 cm depth, with residue retention) and no-tillage systems in a temperate arable system in central Slovenia. Earthworms were sampled annually in early October from three adjacent fields, each following the same three-year crop rotation (maize—winter cereal + cover crop—soybeans), using a combination of hand-sorting and allyl isothiocyanate (AITC) extraction. Results showed that reduced tillage practices significantly increased both earthworm biomass and abundance compared to conventional ploughing. However, a significant interaction between tillage and year was observed, with a sharp decline in earthworm abundance and mass in 2022, likely driven by a combination of 2022 summer tillage prior to cover crop sowing and extreme drought conditions. Juvenile earthworms were especially affected, with their proportion decreasing from 62% to 34% in ploughed plots and from 63% to 26% in conservation tillage plots. Despite interannual fluctuations, no-till showed the lowest variability in earthworm population. Long-term monitoring is essential to disentangle management and environmental effects and to inform resilient soil management strategies. Full article

This review comprehensively examines the application of deep learning technologies across the entire soybean production chain, encompassing areas such as disease and pest identification, weed detection, crop phenotype recognition, yield prediction, and intelligent operations. By systematically analyzing mainstream deep learning models, optimization strategies (e.g., model lightweighting, transfer learning), and sensor data fusion techniques, the review identifies their roles and performances in complex agricultural environments. It also highlights key challenges including data quality limitations, difficulties in real-world deployment, and the lack of standardized evaluation benchmarks. In response, promising directions such as reinforcement learning, self-supervised learning, interpretable AI, and multi-source data fusion are proposed. Specifically for soybean automation, future advancements are expected in areas such as high-precision disease and weed localization, real-time decision-making for variable-rate spraying and harvesting, and the integration of deep learning with robotics and edge computing to enable autonomous field operations. This review provides valuable insights and future prospects for promoting intelligent, efficient, and sustainable development in soybean production through deep learning. Full article

This study presents an intelligent method for evaluating the risk of Asian rust (Phakopsora pachyrhizi) based on its development stage in soybean crops (Glycine max (L.) Merrill). It has been designed using smart computer systems supported by image processing, environmental sensor data, and an embedded model for evaluating favorable conditions for disease progression within crop areas. The approach also includes the use of machine learning techniques and a Markov chain algorithm for data fusion, aimed at supporting decision-making in agricultural management. Rules derived from time-series data are employed to enable scenario prediction for risk evaluation related to disease development. Measured data are stored in a customized system designed to support virtual monitoring, facilitating the evaluation of disease severity stages by farmers and enabling timely management actions. Full article

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article

Soybean frogeye leaf spot (FLS), a serious soybean disease, causes severe yield losses in the largest production regions of China. However, both conventional field monitoring and machine learning algorithms remain challenged in achieving rapid and accurate detection. In this study, an HSDT-TabNet model was proposed for the grading of soybean FLS under field conditions by analyzing unmanned aerial vehicle (UAV)-based hyperspectral data. This model employs a dual-path parallel feature extraction strategy: the TabNet path performs sparse feature selection to capture fine-grained local discriminative information, while the hierarchical soft decision tree (HSDT) path models global nonlinear relationships across hyperspectral bands. The features from both paths are then dynamically fused via a multi-head attention mechanism to integrate complementary information. Furthermore, the overall generalization ability of the model is improved through hyperparameter optimization based on the tree-structured Parzen estimator (TPE). Experimental results show that HSDT-TabNet achieved a macro-accuracy of 96.37% under five-fold cross-validation. It outperformed the TabTransformer and SVM baselines by 2.08% and 2.23%, respectively. For high-severity cases (Level 4–5), the classification accuracy exceeded 97%. This study provides an effective method for precise field-scale crop disease monitoring. Full article

Mycotoxins are a large family of secondary metabolites produced by filamentous fungi species that may be present in food following fungal growth. Mycotoxins are found in a variety of crops, including wheat, millet, maize, sorghum, peanut, soybean, and their by-products. In recent years, the consumption of ready-to-eat food (RTE) has increased exponentially. An increasing number of consumers have elected to purchase and consume ready-made meals, a choice that allows for a more expedient and convenient dining experience. The aim of this review was to investigate recent literature to find a link between the consumption of mycotoxin-contaminated RTEs, modern detection methods (artificial intelligence), and potential health risks to consumers. The regular exchange of information between the Member States and the European Community (EU) concerning the monitoring of contaminants and undesirable chemical substances, and the subsequent communication of the findings to the EFSA, provides the foundation for the evolution of the legislative framework with the objective of enhancing food safety and reducing the risks associated with the consumption of food. It is imperative that governments, the food industry, and the scientific community collaborate to reduce this risk and ensure consumer safety. Full article

Despite growing importance, agricultural landscapes face threats, like fragmentation, shrinkage, and degradation, due to climate change. Although remote sensing and GIS are widely used in monitoring croplands, integrating machine learning, remote sensing, GIS, and landscape metrics for the holistic management of this landscape remains underexplored. Thus, this study monitored crop patterns using random forest (94% accuracy), the role of geographical factors (such as elevation, aspect, slope, maximum and minimum temperature, rainfall, cation exchange capacity, NPK, soil pH, soil organic carbon, soil type, soil water content, proximity to drainage, proximity to market, proximity to road, population density, and profit per hectare production), diversity, combinations, and fragmentation using landscape metrics and a fragmentation index. Findings revealed that slope, rainfall, temperature, and profit per hectare production emerged as significant drivers in shaping crop patterns. However, anthropogenic drivers became deciding factors during spatial overlaps between crop suitability zones. Rice belts were the least fragmented and highly productive with a risk of monoculture. Croplands with a combination of soybean, black grams, and maize were highly fragmented, despite having high diversity with comparatively less production per field. These diverse fields were providing higher profits and low risks of crop failure due to the crop combinations. Equally, intercropping balanced the nutrient uptakes, making the practice sustainable. Thus, it can be suggested that productivity and diversity should be prioritized equally to achieve sustainable land use. The development of the PCA-weighted fragmentation index offers an efficient tool to measure fragmentation across similar agricultural regions, and the integrated approach provides a scalable framework for holistic management, sustainable land use planning, and precision agriculture. Full article

Timely diagnosis of soybean diseases is essential to protect yields and limit global economic loss, yet current deep learning approaches suffer from small, imbalanced datasets, single-organ focus, and limited interpretability. We propose MaxViT-XSLD (MaxViT XAI-Seed–Leaf-Diagnostic), a Vision Transformer that integrates multiaxis attention with MBConv layers to jointly classify soybean leaf and seed diseases while remaining lightweight and explainable. Two benchmark datasets were upscaled through elastic deformation, Gaussian noise, brightness shifts, rotation, and flipping, enlarging ASDID from 10,722 to 16,000 images (eight classes) and the SD set from 5513 to 10,000 images (five classes). Under identical augmentation and hyperparameters, MaxViT-XSLD delivered 99.82% accuracy on ASDID and 99.46% on SD, surpassing competitive ViT, CNN, and lightweight SOTA variants. High PR-AUC and MCC values, confirmed via 10-fold stratified cross-validation and Wilcoxon tests, demonstrate robust generalization across data splits. Explainable AI (XAI) techniques further enhanced interpretability by highlighting biologically relevant features influencing predictions. Its modular design also enables future model compression for edge deployment in resource-constrained settings. Finally, we deploy the model in SoyScan, a real-time web tool that streams predictions and visual explanations to growers and agronomists. These findings establishes a scalable, interpretable system for precision crop health monitoring and lay the groundwork for edge-oriented, multimodal agricultural diagnostics. Full article