Search Results (302)

Knowledge of weed community structure in agricultural systems is important for sustainable management, especially for evaluating the effects of different herbicides on soybean–corn succession crops. This study evaluated, over two crop seasons, weed community structure in response to chemical weed management strategies for soybean–corn succession in Brazil’s Triângulo Mineiro region. Phytosociological surveys of the weed community were conducted during harvest periods throughout the experimental phase, with referenced data for generating spatial distribution maps of biomass and density of the main present species. The survey identified 33 weed species, predominantly from the Poaceae and Asteraceae families. Regardless of the management system, the total weed biomass was lower in corn crops compared to soybean crops. In management systems using six different herbicides, the IVI of Commelina benghalensis was the lowest due to greater diversification of herbicide mechanisms of action. The results demonstrate that chemical weed management strategies strongly influence weed community structure, with significant effects on weed community structure and evenness in intensive agricultural regions. These changes also have implications for resistance management. Full article

A method utilizing quaternion principal component analysis (QPCA) for three-dimensional fluorescence spectral (3D FS) feature extraction is employed to identify frying oil in edible oil. Particle swarm optimization partial least squares support vector machine (PSO-LSSVR) is utilized for detecting frying oil concentration. The study includes rapeseed oil, soybean oil, peanut oil, blending oil, and corn oil samples. Adulteration involves adding frying oil to these edible oils at concentrations of 0%, 5%, 10%, 30%, 50%, 70%, and 100%. Firstly, the F7000 fluorescence spectrometer is employed to measure the 3D FS of the adulterated edible oil samples, resulting in the generation of contour maps and 3D FS projections. The excitation wavelengths utilized in these measurements are 360 nm, 380 nm, and 400 nm, while the emission wavelengths span from 220 nm to 900 nm. Secondly, leveraging the automatic peak-finding function of the spectrometer, a quaternion parallel representation model of the 3D FS data for frying oil in edible oil is established using the emission spectra data corresponding to the aforementioned excitation wavelengths. Subsequently, in conjunction with the K-nearest neighbor classification (KNN), three feature extraction methods—summation, modulus, and multiplication quaternion feature extraction—are compared to identify the optimal approach. Thirdly, the extracted features are input into KNN, particle swarm optimization support vector machine (PSO-SVM), and genetic algorithm support vector machine (GA-SVM) classifiers to ascertain the most effective discriminant model for adulterated edible oil. Ultimately, a quantitative model for adulterated edible oil is developed based on partial least squares regression, PSO-SVR and PSO-LSSVR. The results indicate that the classification accuracy of QPCA features combined with PSO-SVM achieved 100%. Furthermore, the PSO-LSSVR quantitative model exhibited the best performance. Full article

Soybean is a key global crop for food and oil production, playing a vital role in ensuring food security and supplying plant-based proteins and oils. Accurate information on soybean distribution is essential for yield forecasting, agricultural management, and policymaking. In this study, we developed an Enhanced Chlorophyll Index (NRLI) to improve the separability between soybean and maize—two spectrally similar crops that often confound traditional vegetation indices. The proposed NRLI integrates red-edge, near-infrared, and green spectral information, effectively capturing variations in chlorophyll and canopy water content during key phenological stages, particularly from flowering to pod setting and maturity. Building upon this foundation, we further introduce a pixel-wise compositing strategy based on the peak phase of NRLI to enhance the temporal adaptability and spectral discriminability in crop classification. Unlike conventional approaches that rely on imagery from fixed dates, this strategy dynamically analyzes annual time-series data, enabling phenology-adaptive alignment at the pixel level. Comparative analysis reveals that NRLI consistently outperforms existing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Greenness and Water Content Composite Index (GWCCI), across representative soybean-producing regions in multiple countries. It improves overall accuracy (OA) by approximately 10–20 percentage points, achieving accuracy rates exceeding 90% in large, contiguous cultivation areas. To further validate the robustness of the proposed index, benchmark comparisons were conducted against the Random Forest (RF) machine learning algorithm. The results demonstrated that the single-index NRLI approach achieved competitive performance, comparable to the multi-feature RF model, with accuracy differences generally within 1–2%. In some regions, NRLI even outperformed RF. This finding highlights NRLI as a computationally efficient alternative to complex machine learning models without compromising mapping precision. This study provides a robust, scalable, and transferable single-index approach for large-scale soybean mapping and monitoring using remote sensing. Full article

Annual wild soybean, the ancestor of cultivated soybean, underwent a significant increase in seed oil content during domestication. To elucidate the genetic basis of this change, a chromosome segment substitution line population (177 lines) constructed with cultivated soybean NN1138-2 as recipient and wild soybean N24852 as donor was used in this study. Phenotypic evaluation across three distinct environments led to the identification of two major QTL/segments, qOC14 on chromosome 14 and qOC20 on chromosome 20, which collectively explained 39.46% of the phenotypic variation, with individual contributions of 17.87% and 21.59%, respectively. Both wild alleles exhibited negative additive effects, with values of −0.35% and −0.42%, respectively, consistent with the inherently low oil content of wild soybeans. Leveraging transcriptome and genome data from the two parents, two candidate genes were predicted. Notably, Glyma.14G179800 is a novel candidate gene encoding a PHD-type zinc finger domain-containing protein, and the hap-A haplotype exhibits a positive effect on oil content. In contrast, Glyma.20G085100 is a reported POWR1 gene, known to regulate protein and oil content. Our findings not only validate the role of known gene but, more importantly, unveil a new candidate gene, offering valuable genetic resources and theoretical targets for molecular breeding of high-oil soybean. Full article

Mature soybean pods exhibit high homogeneity in color and texture relative to straw and dead leaves, and instances are often densely occluded, posing significant challenges for accurate field segmentation. To address these challenges, this paper constructs a high-quality field-based mature soybean dataset and proposes an adaptive Transformer-based network, PodFormer, to improve segmentation performance under homogeneous backgrounds, dense distributions, and severe occlusions. PodFormer integrates three core innovations: (1) the Adaptive Wavelet Detail Enhancement (AWDE) module, which strengthens high-frequency boundary cues to alleviate weak-boundary ambiguities; (2) the Density-Guided Query Initialization (DGQI) module, which injects scale and density priors to enhance instance detection in both sparse and densely clustered regions; and (3) the Mask Feedback Gated Refinement (MFGR) layer, which leverages mask confidence to adaptively refine query updates, enabling more accurate separation of adhered or occluded instances. Experimental results show that PodFormer achieves relative improvements of 6.7% and 5.4% in mAP50 and mAP50-95, substantially outperforming state-of-the-art methods. It further demonstrates strong generalization capabilities on real-world field datasets and cross-domain wheat-ear datasets, thereby providing a reliable perception foundation for structural trait recognition in intelligent soybean harvesting systems. Full article

This paper proposed a two-level hybrid stacking model for the classification of crops—wheat, soybean, and barley—based on multispectral orthomosaics obtained from uncrewed aerial vehicles. The proposed method unites gradient boosting algorithms (LightGBM, XGBoost, CatBoost) and tree ensembles (RandomForest, ExtraTrees, Attention-MLP deep neural network), whose predictions fuse at the meta-level using ExtraTreesClassifier. Spectral channels, along with a wide range of vegetation indices and their statistical characteristics, are used to construct the feature space. Experiments on an open dataset showed that the proposed model achieves high classification accuracy (Accuracy ≈ 95%, macro-F1 ≈ 0.95) and significantly outperforms individual algorithms across all key metrics. An analysis of the seasonal dynamics of vegetation indices confirmed the feasibility of monitoring phenological phases and early detection of stress factors. Furthermore, spatial segmentation of orthomosaics achieved approximately 99% accuracy in constructing crop maps, making the developed approach a promising tool for precision farming. The study’s results showed the high potential of hybrid ensembles for scaling to other crops and regions, as well as for integrating them into digital agricultural information systems. Full article

Stem strength is a key factor influencing lodging resistance in soybeans and other crops. To identify quantitative trait loci (QTLs) associated with stem strength in soybean, we assessed the peak forces required to break a 20 cm stem base segment for each individual within a collection of 2138 plants from eight F₂ and F₃ segregating populations in 2023 and 2024. These populations were derived from four crosses between soybean varieties with contrasting stem strength. Most populations exhibited an approximately normal distribution of stem strength. Using BSA-seq, we identified 17 QTLs associated with stem strength from four populations. Among these, one QTL overlapped with a previously reported locus, while the remaining 16 represented novel loci. Notably, nine loci overlapped with known lodging QTLs, suggesting a genetic relationship between stem strength and lodging. Three QTLs were repeatedly detected in multiple populations, indicating their stability. Further linkage mapping with molecular markers confirmed these three stable QTLs. Among them, qSS10 and qSS19-2 were identified as major QTLs, refined to 1.06 Mb and 1.54 Mb intervals, with phenotypic variation explained (PVE) 23.31–25.15% and 14.21–19.93%, respectively. Within these stable QTL regions, we identified 13 candidate genes and analyzed their sequence variation and expression profiles. Collectively, our findings provide a valuable foundation for future research on stem strength in soybeans and reveal novel genetic loci and candidate genes that may be utilized for the genetic improvement of soybean lodging resistance and yield stability. Full article

Phosphorus (P) characteristics significantly affect crop yield and P use efficiency (PUE). It is unclear whether different types of acidic phosphate fertilizers can enhance the availability of phosphorus in liming soil and soybean yields. In this field experiment in 2022 and 2023 in Xinjiang, China, four phosphate fertilization treatments, including no phosphate fertilization (CK), application of monoammonium phosphate (MAP), application of urea phosphate (UP), and application of a mixture of monoammonium phosphate and urea phosphate (8:2, M8U2), were designed. Then, the impacts of the four phosphate treatments on the PUE, growth, and yield of the high-oil soybean variety Kennong 23 under drip irrigation were explored. The results showed that the application of phosphate fertilizers significantly increased the soil inorganic P, available P, and total P content compared with CK, promoting the growth and yield formation of soybeans. The soil Ca2-P content of the UP treatment was higher than that of the MAP treatment. The soil Ca8-P content of the M8U2 treatment was higher than that of the MAP treatment, but the soil phosphorus fixation was lower. The soil available P content, soybean plant P accumulation, leaf photosynthetic capacity, and dry matter accumulation all reached the maximum in the M8U2 treatment. The soybean yield, net revenue, and PUE of the M8U2 treatment were 6.04%, 9.37%, and 14.16% higher than those of the MAP treatment, and 7.64%, 16.59%, and 23.50% higher than those of the UP treatment, respectively. Therefore, the combined application of acidic phosphate fertilizers (MAP and UP) can increase soil available P content and plant P absorption in liming soil and stimulate photosynthesis, enhancing soybean yield and PUE. This study will provide a technical reference for the P reduction and soybean yield enhancement in arid areas. Full article

Diceraeus melacanthus (Dallas, 1851) (Hemiptera: Pentatomidae) has become a key pest in Brazilian maize production, particularly during seedling establishment. This study estimated its lower and upper developmental thresholds (Tb and Tsup), thermal constant (K), and degree-day requirements, and used these parameters to model the potential annual generations (PAG) across the Mato Grosso do Sul, Paraná, and São Paulo states. Biological parameters were calculated from controlled laboratory assays, and historical meteorological datasets were combined with regression models and spatial analyses to generate phenology maps of PAG. Results indicated marked regional differences: Mato Grosso do Sul presented the highest potential, averaging eleven generations per year, São Paulo showed intermediate values with nine generations, and Paraná exhibited the lowest, with approximately seven generations annually. Latitude exerted the strongest influence on PAG, while altitude contributed the least. These findings are consistent with the known adaptability of D. melacanthus to warmer climates and highlight its capacity to persist in no-tillage soybean–maize systems and areas with volunteer plants. The results provide a predictive framework for assessing population risk and may support decision-making in integrated pest management. Further studies on host range, phenology, and distribution are required to anticipate future expansions across South America. Full article

Plant tissues exhibit a layered architecture that makes spatial context decisive for interpreting transcriptional changes. This review explains why the location of gene expression is as important as its magnitude and synthesizes advances uniting single-cell/nucleus RNA-seq with spatial transcriptomics in plants. Surveyed topics include platform selection and material preparation; plant-specific sample processing and quality control; integration with epigenomic assays such as single-nucleus Assay for Transposase-Accessible Chromatin using sequencing (ATAC) and Multiome; and computational workflows for label transfer, deconvolution, spatial embedding, and neighborhood-aware cell–cell communication. Protoplast-based single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling but introduces dissociation artifacts and cell-type biases, whereas ingle-nucleus RNA sequencing (snRNA-seq) improves the representation of recalcitrant lineages and reduces stress signatures while remaining compatible with multiomics profiling. Practical guidance is provided for mitigating ambient RNA, interpreting organellar and intronic metrics, identifying doublets, and harmonizing batches across chemistries and studies. Spatial platforms (Visium HD, Stereo-seq, bead arrays) and targeted imaging (Single-molecule fluorescence in situ hybridization (smFISH), Hairpin-chain-reaction FISH (HCR-FISH), Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH)) are contrasted with plant-specific adaptations and integration pipelines that anchor dissociated profiles in anatomical coordinates. Recent atlases in Arabidopsis, soybean, and maize illustrate how cell identities, chromatin accessibility, and spatial niches reveal developmental trajectories and stress responses jointly. A roadmap is outlined for moving from atlases to interventions by deriving gene regulatory networks, prioritizing cis-regulatory targets, and validating perturbations with spatial readouts in crops. Together, these principles support a transition from descriptive maps to mechanism-informed, low-pleiotropy engineering of agronomic traits. Full article

Accurate soybean mapping is critical for food–oil security and cropping assessment, yet spatiotemporal heterogeneity arising from fragmented parcels and phenological variability reduces class separability and robustness. This study aims to deliver a high-resolution, reusable pipeline and quantify the marginal benefits of feature selection and architecture design. We built a full-season multi-temporal Sentinel-1/2 stack and derived candidate optical/SAR features (raw bands, vegetation indices, textures, and polarimetric terms). Jeffries–Matusita (JM) distance was used for feature–phase joint selection, producing four comparable feature sets. We propose a lightweight APM-UNet: an Attention Sandglass Layer (ASL) in the shallow path to enhance texture/boundary details, and a Parallel Vision Mamba layer (PVML with Mamba-SSM) in the middle/bottleneck to model long-range/global context with near-linear complexity. Under a unified preprocessing and training/evaluation protocol, the four feature sets were paired with U-Net, SegFormer, Vision-Mamba, and APM-UNet, yielding 16 controlled configurations. Results showed consistent gains from JM-guided selection across architectures; given the same features, APM-UNet systematically outperformed all baselines. The best setup (JM-selected composite features + APM-UNet) achieved PA 92.81%, OA 97.95, Kappa 0.9649, Recall 91.42%, IoU 0.7986, and F1 0.9324, improving PA and OA by ~7.5 and 6.2 percentage points over the corresponding full-feature counterpart. These findings demonstrate that JM-guided, phenology-aware features coupled with a lightweight local–global hybrid network effectively mitigate heterogeneity-induced uncertainty, improving boundary fidelity and overall consistency while maintaining efficiency, offering a potentially transferable framework for soybean mapping in complex agricultural landscapes. Full article

Soybean seed composition, particularly the oil and protein content of the seed, has been a longstanding focus of research due to the economic and nutritional importance of these components for both feed and industrial applications. Through forward genetic screening of a mutagenized population derived from the soybean cultivar Williams-82, a mutant line designated PID 17238 was identified for high seed oil content. This phenotype is associated with a decrease in levels of protein with respect to Williams-82. The phenotype was mapped to chromosome 2 to a region near Satt459. Fine mapping and whole-genome resequencing were used to identify the causative mutation. Analysis of the resequencing data within the candidate region uncovered 55 sequence variants. Glyma.02G270800 contained a single nucleotide polymorphism (SNP) within the coding sequence. Glyma.02G270800 encodes a lysin motif (LysM) receptor-like kinase previously reported in the literature as GmNFR1α. Importantly, this locus is allelic to the well-characterized rj1 locus, a recessive mutation known to cause a non-nodulating phenotype in soybean. Nodulation in soybeans, which enables nitrogen fixation, is crucial for protein synthesis in seeds, and the lack of nodulation may explain the lower protein content in PID 17238. Full article

Cold pressing is a sustainable oil extraction method that operates without chemical solvents, requires relatively low energy input, and preserves bioactive compounds, making it a recognized green technology in line with circular economy principles. By enabling full utilization of raw materials and valorization of by-products, it supports resource efficiency, waste reduction, and the development of bio-based products. This study provides the first comprehensive mapping of Serbia’s small-scale cold-pressed oil producers, based on data from the Central Register of Food Business Operators, local inspectorates, agricultural fairs, and social media, classified according to NUTS 2024 statistical regions. A total of 55 producers were identified, with over 60% operating as artisanal units (≤15 t/year), typically using screw presses of 20–50 kg/h capacity. Pumpkin seed was the most common raw material (30 producers), followed by sesame (21), hazelnut (20), sunflower (19), and flaxseed (19), while niche oils such as jojoba, argan, and rosehip were produced on a smaller scale. Medium and large facilities (>15 t/year) were concentrated in Vojvodina and central Serbia, focusing on high-volume seeds like sunflower and soybean. Integration of green screw press technologies, zero-kilometer supply chains, and press cake valorization positions this sector as a driver of rural development, biodiversity preservation, and environmental sustainability, providing a strong basis for targeted policy support and process optimization. Full article

Phytophthora root rot (PRR), which is caused by the oomycete pathogen Phytophthora sojae (P. sojae), is one of the most devastating diseases affecting global soybean production. The deployment of resistance (Rps) genes through molecular breeding is a sustainable strategy to control this disease. In this study, we finely mapped a novel resistance gene using two recombinant inbred line (RIL) populations: one comprising 248 F_8:11 lines from a cross between the resistant cultivar ‘Guizao 1’ and the susceptible ‘B13’, and another consisting of 196 F_7:8 lines from a cross between ‘Wayao’ (resistant) and ‘Huachun 2’ (susceptible). The gene in ‘Guizao 1’, designated as Rps15, was delimited to a 78 kb genomic interval on chromosome 3 (bin31), spanning the physical positions from 4,292,416 to 4,370,772 bp. This region contains eight predicted genes. Similarly, the resistance locus in ‘Wayao’ was mapped to a broader region on chromosome 3 (approximately 324 kb; 3,968,039–4,292,863 bp), which encompasses 16 genes. Expression analysis via quantitative real-time PCR of the candidate genes suggested that Glyma.03g036000 is likely involved in the resistance response to PRR. The fine mapping of this novel Rps locus provides a foundation for the future cloning of Rps15 and can be expected to accelerate the development of P. sojae-resistant soybean cultivars through marker-assisted selection. Full article

Seedling-stage weeds are one of the key factors affecting the crop growth and yield formation of soybean. Accurate detection and density mapping of these weeds are crucial for achieving precise weed management in agricultural fields. To overcome the limitations of traditional large-scale uniform herbicide application, this study proposes an improved YOLOv11n-based method for weed detection and spatial distribution mapping by integrating low-altitude UAV imagery with field elevation data. The second convolution in the C3K2 module was replaced with Wavelet Convolution (WTConv) to reduce complexity. A SENetv2-based C2PSA module was introduced to enhance feature representation and context fusion with minimal parameter increase. Soft-NMS-SIoU replaced traditional NMS, improving detection accuracy and robustness for dense overlaps. The improved YOLOv11n algorithm achieved a 3.4% increase in mAP@50% on the test set, outperforming the original YOLOv11n in FPS, while FLOPs and parameter count increased by only 1.2% and 0.2%, respectively. More importantly, the model reliably detected small grass weeds with morphology highly similar to soybean seedlings, which were undetectable by the original model, thus meeting agricultural production monitoring requirements. In addition, the pixel-level weed detection results from the model were converted into coordinates and interpolated using Kriging in ArcGIS (10.8.1) Pro to generate continuous weed density maps, resulting in high-resolution spatial distribution maps directly applicable to variable-rate spraying equipment. The proposed approach greatly improves both the precision and operational efficiency of weed detection and management across large agricultural fields, providing scientific support for intelligent variable-rate spraying using plant protection UAVs and ground-based sprayers, thereby promoting sustainable agriculture. Full article