Salt stress impedes normal development, compromises plant quality, and reduces crop yield. The germination phase in soybean marks the initial stage of its growth cycle. Characterizing salt tolerance during this period can help stimulate soybean growth in natural environments and aid the rapid screening of salt-tolerant soybean varieties. Our study characterized the salt tolerance of 36 soybean germplasms in culture dishes during the germination period. Soybeans were subjected to varying concentrations (0, 60, 120, and 180 mmol/L) of NaCl solution to simulate diverse levels of salt stress, and parameters such as germination energy, germination rate, and root length were measured. Statistical techniques such as analysis of variance, membership function, cluster analysis, and quadratic regression equations were used, and the salt tolerance of these 36 soybean germplasms was determined. The critical indicators and the most effective screening concentration for assessing the germination salt tolerance of soybean were identified. Soybeans tolerated low salt concentrations; however, salt concentrations greater than 120 mmol/L significantly inhibited germination indicators. The germination rate, germination vigor, vitality index, seed germination index, total fresh weight, and total dry weight could be used to identify salt tolerance. The semi-lethal concentration of soybean was 155.4 mmol/L, and the coefficient of variation was 20.00%, indicating that it could be used as a screening concentration for evaluating salt tolerance during soybean germination. A total of 36 soybean varieties were classified into four salt tolerance levels through cluster analysis. QN-27, QN-35, and QN-36 were highly salt-resistant materials, and QN-2, QN-17, and QN-19 were salt-sensitive materials. Characterizing salt tolerance during soybean germination can facilitate the selection and breeding of salt-tolerant soybean varieties. Future research utilizing this approach can aid in the selection of soybean varieties with salinity tolerance.

Various biotic and abiotic stresses challenge plant growth and production, with salt stress notably impeding normal development, compromising quality, and reducing yield in plants. The objective is to identify salt-tolerant soybean germplasm resources and develop a comprehensive method for assessing salt tolerance during soybean germination. This study involved the utilization of 36 soybean germplasm to induce varying degrees of salt stress through the application of 0, 60, 120, and 180 mmol/L NaCl solutions, employing the Petri dish germination identification method to assess nine phenotypic indices, such as germination potential, rate, and radicle length. The salt tolerance of soybean germination was comprehensively assessed using one-way ANOVA, multi-factor ANOVA, principal component analysis, affiliation function, quadratic regression equation analysis, and cluster analysis. These methods were employed to investigate the impact of varied NaCl concentrations on different germination indices, determine the best screening parameters for identifying soybean germination salt tolerance, and identify soybean germplasm with varying salt tolerances. The results indicated that soybean seeds exhibited tolerance to low salt concentrations, while concentrations exceeding 120 mmol/L led to significant inhibition of germination indicators. Notably, the vitality index and radicle length were highly sensitive indicators reflecting the impact of salt stress on seed germination. By analyzing the quadratic regression equation correlating the germination index and the salt tolerance coefficient, an LC50 value of 155.4 mmol/L with a coefficient of variation of 20.00% was derived. This value serves as a viable screening concentration for identifying salt tolerance during the soybean germination stage. Additionally, clustering analysis categorized 36 soybean varieties into 4 salt tolerance levels, with QN-27, QN-35, and QN-36 demonstrating high salt tolerance, and QN-2, QN-17, and QN-19 were classified as salt-sensitive materials.

The accurate identification and classification of soybean mutant lines is essential for developing new plant varieties through mutation breeding. However, most existing studies have focused on the classification of soybean varieties. Distinguishing mutant lines solely by their seeds can be challenging due to their high genetic similarities. Therefore, in this paper, we designed a dual-branch convolutional neural network (CNN) composed of two identical single CNNs to fuse the image features of pods and seeds together to solve the soybean mutant line classification problem. Four single CNNs (AlexNet, GoogLeNet, ResNet18, and ResNet50) were used to extract features, and the output features were fused and input into the classifier for classification. The results demonstrate that dual-branch CNNs outperform single CNNs, with the dual-ResNet50 fusion framework achieving a 90.22 ± 0.19% classification rate. We also identified the most similar mutant lines and genetic relationships between certain soybean lines using a clustering tree and t-distributed stochastic neighbor embedding algorithm. Our study represents one of the primary efforts to combine various organs for the identification of soybean mutant lines. The findings of this investigation provide a new path to select potential lines for soybean mutation breeding and signify a meaningful advancement in the propagation of soybean mutant line recognition technology.