Vegetative soybean (maodou or edamame) serves as a nutrient-rich food source with significant potential for mitigating global nutritional deficiencies. This study undertook a thorough examination of the nutritional profiles and transcriptomic landscapes of six soybean cultivars, including three common cultivars (Heinong551, Heinong562, and Heinong63) and three fresh maodou cultivars (Heinong527, HeinongXS4, and HeinongXS5). Nutrient analysis of the seeds disclosed notable differences in the levels of protein, fat, soluble sugars, vitamin E, calcium, potassium, magnesium, manganese, iron, and zinc across the cultivars. Through comparative transcriptome profiling and RNA sequencing, distinct variations in differentially expressed genes (DEGs) were identified between fresh and traditional maodou cultivars. Functional enrichment analyses underscored the involvement of DEGs in critical biological processes, such as nutrient biosynthesis, seed development, and stress responses. Additionally, association studies demonstrated robust correlations between specific DEG expression patterns and seed nutrient compositions across the different cultivars. Sankey diagrams illustrated that DEGs are strongly linked with seed quality traits, revealing potential molecular determinants that govern variations in nutritional content. The identified DEGs and their relationships with nutritional profiles offer valuable insights for breeding programs focused on developing cultivars with improved nutritional quality, tailored to specific dietary needs or industrial applications.

Accurate and efficient soybean yield prediction is essential for ensuring food security and optimizing agricultural management. Remote-sensing approaches based on a single phenological stage or sensor may not fully capture the physiological and structural dynamics associated with soybean yield formation, thereby constraining prediction accuracy under field-level spatial heterogeneity. This study therefore developed and evaluated an integrated multi-source and multi-temporal UAV-based framework for plot-scale soybean yield prediction under production-field conditions. A UAV platform equipped with multispectral, RGB, and LiDAR sensors was used to extract multidimensional remote-sensing features at four key growth stages: beginning pod, full seed, beginning maturity, and full maturity. By integrating spectral indices, texture metrics, and three-dimensional canopy structural parameters, seven machine-learning algorithms—Ridge, LASSO, Random Forest, MLP, LightGBM, XGBoost, and CatBoost—were evaluated under single-temporal and multi-temporal scenarios. Nonlinear tree-based ensemble models generally achieved higher predictive accuracy than the linear models. On the independent test set, CatBoost performed best under the full multi-temporal, three-sensor fusion scenario, with an R² of 0.816 and an RMSE of 245 kg ha⁻¹. Among individual growth stages, the full seed stage (R6) produced the highest single-stage prediction accuracy. Three-sensor fusion improved prediction relative to the single-source multispectral scheme, and integrating features across phenological stages further improved performance by representing cumulative crop-growth dynamics. The proposed framework provides methodological support for UAV-based precision management and data-driven decision-making in soybean production.

The soybean is a source of several dietary components, including milk, protein, and oil. Cold stress has significantly curtailed soybean growth and yield in large areas and caused a high risk to global food security. The main objective of soybean breeders is to improve soybean resistance to cold stress. Conventional breeding approaches have made significant progress in developing cold tolerance in soybean; however, the high cost and complex genetic mechanism of cold tolerance hindered the large scale of these techniques. Molecular tools like quantitative trait loci (QTL), genome-wide association studies (GWAS), transcription factors (TFs), genetic engineering, and transcriptome have been used to identify cold tolerant genes/QTL and to develop soybean cultivars tolerant to cold stress. Clustered, regularly interspaced short palindromic repeats (CRISPR/Cas9) is used to increase the abiotic stress tolerance in soybean; however, its use to edit the cold tolerance genes in soybean is limited. Mapping of QTL has accelerated the master-assisted selection (MAS) in soybean. This review presents a detailed overview of molecular techniques and their use in developing cold-tolerant soybean cultivars. Using CRISPR/Cas9 would increase the speed of molecular breeding for cold tolerance in soybean. This information will assist soybean researchers in uncovering the basis of cold stress tolerance in soybean and adopting the most suitable way to breed the cold tolerant cultivars which can thrive under the extreme pressure of cold stress.

Background/Objectives: Soybean (Glycine max) productivity is increasingly constrained by soil salinity. WIP transcription factors, a subfamily of C2H2-type zinc finger proteins, regulate cell division, differentiation, and tissue patterning in several plant species, but this gene family had not previously been systematically characterized in soybean or any other major legume crop. This study aimed to identify and characterize the GmWIP gene family genome-wide and evaluate its potential involvement in the soybean salt-stress response. Methods: Genome-wide identification of GmWIP genes was performed using sequence similarity and domain-based searches against the Wm82.gnm4.ann1 reference genome, followed by characterization of physicochemical properties, chromosomal distribution, phylogenetic relationships, gene duplication, conserved motifs, gene structure, and promoter cis-acting elements. Tissue-specific expression was examined using transcriptome data, and GmWIP responses to salt stress were profiled by RT-qPCR in roots, stems, and leaves of a salt-tolerant cultivar (HN531) and a salt-sensitive cultivar (HN563), alongside physiological measurements of oxidative stress and osmotic adjustment. Results: Thirty GmWIP genes were identified, with molecular weights from 26.90 to 57.52 kDa, distributed unevenly across 15 soybean chromosomes, with chromosomes 11, 12, and 13 forming a major hotspot (53.3% of the family). Duplication analysis detected 54 reconciled segmental duplicate gene pairs, all exhibiting Ka/Ks values < 1 (ranging from 0.0351 to 0.4471; mean 0.214), consistent with purifying selection acting on this gene set. GmWIP promoters were enriched for ABRE, MBS, and MeJA cis-acting elements. RT-qPCR showed genotype- and tissue-dependent differential expression under salt stress (e.g., up to 14.9-fold induction of GmWIP22 in HN531 stems), paralleled by superior proline accumulation (+45%), soluble sugars, and CAT activity (+38%) alongside reduced MDA accumulation in the tolerant cultivar. Conclusions: The GmWIP gene family has expanded substantially in soybean relative to previously characterized species and shows genotype-dependent transcriptional responses to salt stress, suggesting that specific GmWIP members are candidate regulators of salt tolerance and warrant further functional investigation.