Soybean gene co-expression network analysis identifies two co-regulated gene modules associated with nodule formation and development

Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage

Soybean (Glycine max (L.) Merr.) is a major source of oil and protein for human food and animal feed; however, soybean crops face diverse factors causing damage, including pathogen infections, environmental shifts, poor fertilization, and incorrect pesticide use, leading to reduced yields. Identifying the level of leaf damage aids yield projections, pesticide, and fertilizer decisions. Deep learning models (DLMs) and neural networks mastering tasks from abundant data have been used for binary healthy/unhealthy leaf classification. However, no DLM predicts and categorizes soybean leaf damage severity (five levels) for tailored pesticide use and yield forecasts. This paper introduces a novel DLM for accurate damage prediction and classification, trained on 2930 near-field soybean leaf images. The model quantifies damage severity, distinguishing healthy/unhealthy leaves and offering a comprehensive solution. Performance metrics include accuracy, precision, recall, and F1-score. This research presents a robust DLM for soybean damage assessment, supporting informed agricultural decisions based on specific damage levels and enhancing crop management and productivity.

Overexpression of soybean GmNAC19 and GmGRAB1 enhances root growth and water-deficit stress tolerance in soybean

Soybean (Glycine max) is an important crop in agricultural production where water shortage limits yields in soybean. Root system plays important roles in water-limited environments, but the underlying mechanisms are largely unknown. In our previous study, we produced a RNA-seq dataset generated from roots of soybean at three different growth stages (20-, 30-, and 44-day-old plants). In the present study, we performed a transcriptome analysis of the RNA-seq data to select candidate genes with probable association with root growth and development. Candidate genes were functionally examined in soybean by overexpression of individual genes using intact soybean composite plants with transgenic hairy roots. Root growth and biomass in the transgenic composite plants were significantly increased by overexpression of the GmNAC19 and GmGRAB1 transcriptional factors, showing up to 1.8-fold increase in root length and/or 1.7-fold increase in root fresh/dry weight. Furthermore, greenhouse-grown transgenic composite plants had significantly higher seed yield by about 2-fold than control plants. Expression profiling in different developmental stages and tissues showed that GmNAC19 and GmGRAB1 were most highly expressed in roots, displaying a distinct root-preferential expression. Moreover, we found that under water-deficit conditions, overexpression of GmNAC19 enhanced water stress tolerance in transgenic composite plants. Taken together, these results provide further insights into the agricultural potential of these genes for development of soybean cultivars with improved root growth and enhanced tolerance to water-deficit conditions.