Search Results (270)

Sustainable manure recycling in cold climates faces low efficiency and nutrient loss. This study evaluated housefly larva-pretreated manure (HL) for winter swine manure composting in East China, comparing it to sawdust-conditioned (CK2) and untreated manure (CK1). Larval pretreatment converted 12.71% of manure weight into biomass, assimilating 10.69% C, 30.55% N, 8.54% P, and 11.53% K. Harvested larvae contained 53.35% crude protein, with amino acids matching/exceeding fishmeal and soybean meal, while heavy metals were below safety limits. Theoretical annual larval protein yield per unit area (29,530 kg·mu⁻¹·year⁻¹) was 206.5 times higher than soybean crops. During composting, the HL treatment promoted early protease and catalase activation. This enzymatic synergy accelerated organic matter degradation and maturation, achieving a germination index of 147.67% by day 51. Coordinated nitrate and nitrite reductase activity in HL facilitated efficient denitrification, minimizing NO₂⁻ accumulation and N₂O emissions. Consequently, HL composting achieved faster stabilization, enhanced nutrient retention, and greater protein recovery compared to controls. These findings demonstrate that housefly larval pretreatment offers a climate-resilient and scalable strategy for winter manure management and protein valorization, with strong potential for applications in cold and resource-limited agricultural systems worldwide. 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 postharvest quality evaluation of soybeans is essential for preserving product value and meeting industry standards. Traditional inspection methods are often inconsistent, labor-intensive, and unsuitable for high-throughput operations. This study presents a non-destructive soybean classification approach using a simplified reflectance-mode hyperspectral imaging system equipped with a single light source, eliminating the complexity and maintenance demands of dual-light configurations used in prior studies. A spectral–spatial data fusion strategy was developed to classify harvested soybeans into four categories: normal, split, diseased, and foreign materials such as stems and pods. The dataset consisted of 1140 soybean samples distributed across these four categories, with spectral reflectance features and spatial texture attributes extracted from each sample. These features were combined to form a unified feature representation for use in classification. Among multiple machine learning classifiers evaluated, Linear Discriminant Analysis (LDA) achieved the highest performance, with approximately 99% accuracy, 99.05% precision, 99.03% recall and 99.03% F1-score. When evaluated independently, spectral features alone resulted in 98.93% accuracy, while spatial features achieved 78.81%, highlighting the benefit of the fusion strategy. Overall, this study demonstrates that a single-illumination HSI system, combined with spectral–spatial fusion and machine learning, offers a practical and potentially scalable approach for non-destructive soybean quality evaluation, with applicability in automated industrial processing environments. Full article

Soybean (Glycine max L.) is one of the world’s most important sources of plant-based protein, with a protein content exceeding 35–40% (dry basis), along with other essential nutritional benefits. Ideally, soybeans are field-dried to approximately 13% moisture content (wet basis, wb); however, adverse weather conditions can necessitate harvesting at elevated moisture levels sometimes exceeding 20% (wb). In such cases, mechanized drying systems, particularly in northern U.S. regions, become essential for safe storage and quality preservation. This study investigated the effects of drying temperature, airflow rate, and initial moisture content on drying kinetics and kernel integrity using mathematical modeling. Drying behavior was modeled using fractional calculus and compared to the empirical Page model, while kernel cracking and breakage were analyzed using logistic regression. Both fractional and Page models exhibited strong agreement with experimental data (R² = 0.903–0.993). The fractional model achieved superior predictive accuracy, improving RMSE and MAE by 83.7% and 81.2%, respectively, compared to the Page model. Cracking and breakage were more strongly influenced by drying temperature than by initial moisture content, with the greatest quality degradation occurring at high temperatures. Optimal drying conditions were identified as temperatures below 27 °C and initial moisture contents between 19 and 20% (wb), which best preserved kernel quality. Logistic models more accurately predicted breakage than cracking, confirming their effectiveness in assessing mechanical damage during drying. The results affirm the suitability of fractional order models for accurately capturing drying kinetics, while logistic models offer robust performance for evaluating physical quality degradation. These modeling approaches provide a framework for efficient and quality-preserving soybean drying strategies in regions reliant on off-field drying systems. Full article

As one of the world’s leading grain producers, Brazil stands out in soybean and corn production. Accurate estimation of key crop phenological stages is essential for agricultural decision-making, especially considering Brazil’s vast territory, climatic diversity, and increasing frequency of extreme weather events. This study investigated the applicability of the NDVI, EVI, WDRVI, and NDWI, derived from Harmonized Landsat Sentinel-2, to identify crop sowing and harvest dates at the field scale. We extracted the vegetative peak from each vegetation index time series and identified the left and right inflection points around the peak to delineate the crop season. A double-logistic function and a derivative approach were applied to identify the Start of Season, Peak of Season, and End of Season. For both soybeans and corn, the RMSE ranged from 5 to 8 days for sowing dates, while for harvest dates it ranged from 6 to 15 days for corn. Despite these differences, all vegetation indices exhibited robust performance, with Spearman correlation values between 0.56 and 0.84. Our findings indicate that the use of different indices does not have a significant impact on the results, as long as the adjustment of temporal parameters for the phenological metrics is appropriate for each index. Full article

Water deficiency and low water use efficiency severely constrain wheat yield in dryland regions. This study aimed to identify suitable tillage methods and straw management to improve dry matter production, grain yield, and water use efficiency of wheat in the dryland winter wheat–summer bean (hereafter referred to as wheat-soybean) double-cropping system. A long-term located field experiment (onset in October 2009) with two tillage methods—plowing (PT) and rotary tillage (RT)—and two straw management—no straw mulching (NS) and straw mulching (SM)—was conducted at a typical dryland in China. The wheat yield and yield component, dry matter accumulation and translocation characteristics, and water use efficiency were investigated from 2014 to 2018. Straw management significantly affected wheat yield and yield components, while tillage methods had no significant effect. Furthermore, the interaction of tillage methods and straw management significantly affected yield and yield components except for the spike number. RTSM significantly increased the spike number, grains per spike, 1000-grain weight, harvest index, and grain yield by 12.5%, 8.4%, 6.0%, 3.4%, and 13.4%, respectively, compared to PTNS. Likewise, RTSM significantly increased the aforementioned indicators by 14.8%, 10.1%, 7.5%, 3.6%, and 20.5%, compared to RTNS. Mechanistic analysis revealed that, compared to NS, SM not only significantly enhanced pre-anthesis and post-anthesis dry matter accumulation, and pre-anthesis dry matter tanslocation to grain, but also significantly improved pre-sowing water storage, water consumption during wheat growth, water use efficiency, and water-saving for produced per kg grain yield, with the greatest improvements obtained under RT than PT. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) analysis confirmed RTSM’s yield superiority was mainly ascribed to straw-induced improvements in dry matter and water productivity. In a word, rotary tillage with straw mulching could be recommended as a suitable practice for high-yield wheat production in a dryland wheat-soybean double-cropping system. 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

Crop residue management and soil tillage (CRM and ST) are key steps in agricultural production. The effects of different CRM and ST modes on the quality of seedbed, seeding, and harvest yield are not well determined. In this study, the system of maize (Zea mays L.)–soybean (Glycine max (L.) Merr) rotation under ridge-tillage in the semi-arid regions of Northeast China was chosen as the study conditions. Four modes were investigated: deep tillage and seeding (DT and S), stubble field and no-tillage seeding (SF and NTS), three-axis rotary tillage and seeding (TART and S), and shallow rotary tillage and seeding (SRT and S). Results show that the DT and S mode produced the best quality of seedbed and seeding. Among the conservation tillage modes, the SRT and S mode produced the shortest average length of roots and straw, the best uniformity of their distribution in the seedbed, and the highest soybean yield. Both the SRT and S and SF and NTS modes yielded a higher net profit as their cost-effectiveness. When considering only the quality of seedbed and seeding under conservation tillage as a prerequisite, it can be concluded that the SRT and S mode is both advantageous and sustainable. Full article

In this work, bio-based thermoelectric composites were developed using acrylated epoxidized soybean oil (AESO) as the polymer matrix and bismuth telluride (Bi₂Te₃) as the thermoelectric filler. The materials were formulated for both UV-curing and thermal-curing processes, with a focus on Digital Light Processing (DLP) 3D printing. Although UV curing proved ineffective at high filler concentrations due to the light opacity of Bi₂Te₃, thermal curing enabled the fabrication of stable, homogeneously dispersed composites. The samples were thoroughly characterized through rheology, FTIR, TGA, XRD, SEM, and density measurements. Thermoelectric performance was assessed under a 70 °C temperature gradient, with Seebeck coefficients reaching up to 51 µV/K. Accelerated chemical degradation studies in basic media confirmed the degradability of the matrix. The results demonstrate the feasibility of combining additive manufacturing with sustainable materials for low-power thermoelectric energy harvesting applications. Full article

Some herbicides, such as saflufenacil, can persist as residues in sprayer tanks even after cleaning, causing phytotoxicity in sensitive crops. This study aimed to simulate potential injury caused by saflufenacil residues, applied alone or combined with glyphosate, on soybean. The field experiment was conducted using a randomized complete block design with four replicates. The treatments included glyphosate (1440 g ha⁻¹), eight saflufenacil doses ranging from 1.09 to 70.00 g ha⁻¹, each tested alone or combined with glyphosate, and a weed-free control, totaling 18 treatments. Phytotoxicity was assessed at 7, 14, 21, 28, and 35 days after treatment (DAT). Physiological variables were measured at 21 DAT, and grain yield components were evaluated at harvest. Saflufenacil caused increasing phytotoxicity at doses exceeding 4.38 g ha⁻¹ when applied alone and above 2.17 g ha⁻¹ when combined with glyphosate. The highest doses negatively affected soybean physiology and grain yield components. Soybean tolerated up to 2.17 g ha⁻¹ saflufenacil alone and up to 1.09 g ha⁻¹ combined with glyphosate without significant yield loss. These results highlight the importance of thorough and correct cleaning of the sprayer tank and suggest limit residue levels that avoid crop damage, helping to prevent unexpected damage to soybean in crop rotations. Full article

As a vital oil and cereal crop in China, soybean requires efficient and low-loss harvesting to ensure food security and sustainable agricultural development. However, pod-shattering losses during soybean harvesting in Xinjiang remain severe due to low pod moisture content and poor mechanical strength, while existing studies lack a systematic analysis of the interaction mechanism between reeling devices and pods. The current research on soybean harvester headers predominantly focuses on conventional rigid designs, with limited exploration of flexible reel mechanisms and their biomechanical interactions with soybean pods. To address this, this study proposes an optimization method for low-loss harvesting technology based on mechanical-crop interaction mechanisms, integrating dynamic simulation, contact mechanics theory, and field experiments. Texture analyzer tests revealed pod-shattering force characteristics under different compression directions, showing that vertical compression exhibited the highest shattering risk with an average force of 14.3271 N. A collision model between the spring tooth and pods was established based on Hertz contact theory, demonstrating that reducing the elastic modulus of the spring tooth and increasing the contact area significantly minimized mechanical damage. Simulation verified that the PVC-nylon spring tooth reduced the maximum equivalent stress on pods by 90.3%. Furthermore, the trajectory analysis of spring-tooth tips indicated that effective pod-reeling requires a reel speed ratio (Δ) exceeding 1.0. Field tests with a square flexible spring tooth showed that the optimized reel reduced header loss to 1.371%, a significant improvement over conventional rigid teeth. This study provides theoretical and technical foundations for developing low-loss soybean harvesting equipment. Future work should explore multi-parameter collaborative optimization to enhance adaptability in complex field conditions. Full article

Unlike traditional yield mapping, which is conducted using costly yield sensors mounted on combine harvesters to collect post-harvest data, yield potential prediction using remote sensing data is considered a low-cost alternative. In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. Combinations of seven vegetation indices from Sentinel-2 imagery and seven phenology metrics were evaluated for the prediction of maize and soybean yield potential. Ground truth yield data were provided by the Quantile Loss Domain Adversarial Neural Network (QDANN) database, with 1000 samples randomly selected per year from 2019 to 2022 for Iowa and Illinois. Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). Across all evaluations, RF was found to outperform the other models in both cross-validation and final model accuracy metrics. Vegetation index values at peak of season (POS) and phenological timing, expressed as the day of year (DOY) of phenological events, were identified as the most influential covariates for predicting yield potential in particular years for both maize and soybean. Full article

Against the backdrop of global food security challenges, precise pre-harvest yield estimation of large-scale soybean crops is crucial for optimizing agricultural resource allocation and ensuring stable food supplies. This study developed an integrated prediction model for county-level soybean yield forecasting, which combines multi-source remote-sensing data with advanced deep learning techniques. The ant colony optimization-convolutional neural network with gated recurrent units and multi-head attention (ACGM) model showcases remarkable predictive prowess, as evidenced by a coefficient of determination (R²) of 0.74, a root mean square error (RMSE) of 123.94 kg/ha, and a mean absolute error (MAE) of 105.39 kg/ha. When pitted against other models, including the random forest regression (RFR), support vector regression (SVR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, the ACGM model clearly emerges as the superior performer. This study identifies August as the optimal period for early soybean yield prediction, with the model performing best when combining environmental and photosynthetic parameters (ED + PP). The ACGM model demonstrates a good accuracy and generalization ability, providing a practical approach for refined agricultural management. By integrating deep learning with open-source remote-sensing data, this research opens up new avenues for enhancing agricultural decision-making and safeguarding food security. Full article

The study aimed to determine the physical and physicochemical properties of soybean grains using NIR spectroscopy coupled with multivariate data analysis. The experiment was carried out in two stages: first, individual characterization of defects and healthy grains; then, analyses of samples classified into different types (type I, type II, basic standard, and out of type). The centesimal composition of the grains (crude protein, lipids, water content, crude fiber, starch, and ash) was determined by NIR spectroscopy, and the data were analyzed by ANOVA, Scott-Knott test, principal component analysis (PCA), k-means clustering, and Pearson correlation. The results showed significant variations between defects and commercial types in all the variables evaluated (p < 0.05), with an emphasis on germinated grains (higher protein content) and broken grains (higher fiber content). The PCA explained 66.6% of the total variance in the defect sets and 52.2% of the types, allowing the formation of groups defined by the clustering algorithms. Pearson correlations indicated important interactions between the chemical variables, such as the negative correlation between protein and crude fiber (r = −0.73) and between lipids and water content (r = −0.66). It is concluded that the NIR method combined with multivariate modeling allows for the rapid assessment of soybean grain quality in real time, optimizing, reducing waste in, and increasing the efficiency of post-harvest processes. Full article

Precision agriculture facilitates improved management by studying the spatial and temporal variability of soil attributes. Soybean (Glycine max (L.) Merrill) seeds may exhibit distinct quality when produced in different management zones. This study aimed to validate management zones during seed production and identify the variability and spatial distribution of soybean seed physiological quality using geostatistical tools. Management zones were defined based on interpolated maps of soil and vegetation attributes using the Smart Map Plugin (SMP) within the QGIS environment. Post-harvest, the variability of physiological seed quality across different management zones was assessed. Germination, accelerated aging, dry weight, emergence, electrical conductivity, and tetrazolium tests were conducted in a completely randomized design. Soil attributes, initial plant stand, and soybean seed productivity validated the management zones. Physiological seed quality varies across the production field, particularly in terms of vigor, thereby enhancing diagnostics through map interpolation. Geostatistics enable determination of the spatial distribution of soybean seed physiological quality in seed production areas, facilitating decision-making regarding harvest zones. Full article