Expanded agriculture production is required to support the world’s population but can impose substantial environmental and climate change costs, particularly with intensifying animal production and protein demand. Shifting from an animal- to a plant-based protein diet has numerous health benefits. Soybean (Glycine max (L.) Merr.) is a major source of protein for human food and animal feed; improved soybean protein content and amino acid composition could provide high-quality soymeal for animal feed, healthier human foods, and a reduced carbon footprint. Nonetheless, during the soybean genome evolution, a balance was established between the amount of seed protein, oil, and carbohydrate content, burdening the development of soybean cultivars with high proteins. We isolated two high-seed protein (HP) soybean mutants, HP1 and HP2, with improved seed amino acid composition and stachyose content, pointing to their involvement in controlling seed rebalancing phenomenon. HP1 encodes β-conglycinin (GmCG-1) and HP2 encodes Sucrose Binding Protein (GmSBP-1), which are both highly expressed in soybean seeds. Mutations in GmSBP-1, GmCG-1, and the paralog GmCG-2 resulted in increased protein levels, confirming their role as general regulators of seed protein content, amino acid seed composition, and seed vigor. Biodiversity analysis of GmCG and GmSBP across 108 soybean accessions revealed haplotypes correlated with protein and seed carbohydrate content. Furthermore, our data revealed an unprecedented role of GmCG and GmSBP proteins in improving seed vigor, crude protein, and amino acid digestibility. Since GmSBP and GmCG are present in most seed plants analyzed, these genes could be targeted to improve multiple seed traits.

The accurate identification of soybean growth stages is critical for optimizing agricultural interventions, where mistimed treatments can result in yield losses ranging from 2.5% to 40%. Existing deep learning approaches remain limited in scope, targeting isolated developmental phases rather than providing comprehensive phenological coverage. This paper presents a novel object detection architecture DELTA-SoyStage, combining an EfficientNet backbone with a lightweight ChannelMapper neck and a newly proposed DELTA (Denoising Enhanced Lightweight Task Alignment) detection head for soybean growth stage classification. We introduce a dataset of 17,204 labeled RGB images spanning nine growth stages from emergence (VE) through full maturity (R8), collected under controlled greenhouse conditions with diverse imaging angles and lighting variations. DELTA-SoyStage achieves 73.9% average precision with only 24.4 GFLOPs computational cost, demonstrating 4.2× fewer FLOPs than the best-performing baseline (DINO-Swin: 74.7% AP, 102.5 GFLOPs) with only 0.8% accuracy difference. The lightweight DELTA head combined with the efficient ChannelMapper neck requires only 8.3 M parameters—a 43.5% reduction compared to standard architectures—while maintaining competitive accuracy. Extensive ablation studies validate key design choices including task alignment mechanisms, multi-scale feature extraction strategies, and encoder–decoder depth configurations. The proposed model’s computational efficiency makes it suitable for deployment on resource-constrained edge devices in precision agriculture applications, enabling timely decision-making without reliance on cloud infrastructure.