Summary: Soybeans (Glycine max L. Merr.) are a major source of plant‐based protein for human nutrition and livestock feed. Enhancing the protein content of soybean seeds is vital for meeting growing dietary needs and promoting sustainable agricultural practices. In this study, we first performed QTL (Quantitative Trait Loci) mapping analysis and constructed a Soybean Seed Protein Content Co‐expression (Soy‐SPCC) network to identify key genes associated with soybean seed protein accumulation. Next, we investigated the role of High Seed Storage Protein1 (HSSP1) in regulating soybean seed protein content through a comprehensive analysis. Functional validation through overexpression and gene knockout experiments demonstrated that HSSP1, a key component of the Soy‐SPCC network, significantly influences seed storage protein levels. Particularly, HSSP1 enhances the expression of GmCG1 by binding directly to its cis‐acting element, leading to increased protein content in soybean seeds. Furthermore, we performed a molecular module stacking breeding analysis of 120 candidate genes identified from the Soy‐SPCC network, including HSSP1, to identify genetic variations associated with protein content. This study provides a novel perspective on soybean protein regulation. The identification of HSSP1 as a critical regulator offers valuable insights for developing high‐protein soybean varieties and advancing breeding strategies aimed at improving soybean seed quality.

Timely detection of pathogen spores is fundamental to ensuring early intervention and reducing the spread of corn diseases, like northern corn leaf blight, corn head smut, and corn rust. Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. To improve the recognition accuracy of various maize disease spores, this study introduced the YOLOv8s-SPM model by incorporating the space-to-depth and convolution (SPD-Conv) layers, the Partial Self-Attention (PSA) mechanism, and Minimum Point Distance Intersection over Union (MPDIoU) loss function. First, we combined SPD-Conv layers into the Backbone of the YOLOv8s to enhance recognition performance on small targets and low-resolution images. To improve computational efficiency, the PSA mechanism was incorporated within the Neck layer of the network. Finally, MPDIoU loss function was applied to refine the localization performance of bounding boxes. The results revealed that the YOLOv8s-SPM model achieved 98.9% accuracy on the mixed spore dataset. Relative to the baseline YOLOv8s, the YOLOv8s-SPM model yielded a 1.4% gain in accuracy. The improved model significantly improved spore detection accuracy and demonstrated superior performance in recognizing diverse spore types under complex background conditions. It met the demands for high-precision spore detection and filled a gap in intelligent spore recognition for maize, offering an effective starting point and practical path for future research in this field.

The Kunitz trypsin inhibitor (KTI) gene family encompasses a category of trypsin inhibitors, and the KTI proteins are important components of the 2S storage protein fraction in soybeans. In this study, fifty members of the GmKTI family were identified in the soybean genome, and their physicochemical properties, domain compositions, phylogenetic relationships, gene structures, and expression patterns were comprehensively analyzed to explore their impact on soybean seed protein content. The results revealed significant gene expansion within the GmKTI family in soybean. The gene structures and conserved motifs of GmKTI members exhibited both regularity and diversity, with distinct expression patterns across different soybean tissues. Haplotype analysis identified 7 GmKTI genes significantly associated with seed storage protein content, and the combination of superior haplotypes was found to enhance seed storage protein content. This is crucial for the improvement of soybean varieties and the enhancement of storage protein content. Additionally, the GmKTI family demonstrated evolutionary conservation, with its functions likely linked to light induction, biotic stress, and growth development. This study characterizes the structure, expression, genomic haplotypes, and molecular features of the soybean KTI domain for the first time, providing a foundation for functional analyses of the GmKTI domain in soybean and other plants.

The dimensions of organs such as flowers, leaves, and seeds are governed by processes of cellular proliferation and expansion. In soybeans, the dimensions of these organs exhibit a strong correlation with crop yield, quality, and other phenotypic traits. Nevertheless, there exists a scarcity of research concerning the regulatory genes influencing flower size, particularly within the soybean species. In this study, 309 samples of 3 soybean types (123 cultivar, 90 landrace, and 96 wild) were re-sequenced. The microscopic phenotype of soybean flower organs was photographed using a three-eye microscope, and the phenotypic data were extracted by means of computer vision. Pearson correlation analysis was employed to assess the relationship between petal and seed phenotypes, revealing a strong correlation between the sizes of these two organs. Through GWASs, SNP loci significantly associated with flower organ size were identified. Subsequently, haplotype analysis was conducted to screen for upstream and downstream genes of these loci, thereby identifying potential candidate genes. In total, 77 significant SNPs associated with vexil petals, 562 significant SNPs associated with wing petals, and 34 significant SNPs associated with keel petals were found. Candidate genes were screened by candidate sites, and haplotype analysis was performed on the candidate genes. Finally, the present investigation yielded 25 and 10 genes of notable significance through haplotype analysis in the vexil and wing regions, respectively. Notably, Glyma.07G234200, previously documented for its high expression across various plant organs, including flowers, pods, leaves, roots, and seeds, was among these identified genes. The research contributes novel insights to soybean breeding endeavors, particularly in the exploration of genes governing organ development, the selection of field materials, and the enhancement of crop yield. It played a role in the process of material selection during the growth period and further accelerated the process of soybean breeding material selection.