The damage to crops due to drought is severe, and it is further exacerbated by global warming. Therefore, efforts to breed drought-resistant cultivars are being actively conducted. Various research to develop drought-resistant and sustainable cultivars of soybean (Glycine max L.), the most popular legume crop worldwide, are conducted, but it is biased toward the reproductive stages that are most severely affected by drought. There are few studies on the effect of drought stress on the vegetative stages due to a relatively small effect on the final product, but it is an important factor. Therefore, plants were subjected to 5% soil moisture for 14 days to assess the effect of drought at three different vegetative stages, and we measured the number of nodes of the main stem, the total number of nodes, and the number of pods. Although most of the unstressed soybean plants were dominant against the drought-stressed plants in certain cultivars, side node formation or the total number of nodes and the number of pods were reversed. Our results suggested that investigated response of phenotypic traits might be used as a selection indicator for drought-resistant soybean cultivars with further research and sustainability.

Drought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to solve this problem, which is considered the most efficient method for crop producers. To accelerate breeding cycles, genetic engineering and high-throughput phenotyping technologies have replaced conventional breeding methods. However, the current novel phenotyping method still needs to be optimized by species and varieties. Therefore, we aimed to assess the most appropriate and effective phenotypes for evaluating drought stress by applying a high-throughput image-based method on the nested association mapping (NAM) population of soybeans. The acquired image-based traits from the phenotyping platform were divided into three large categories—area, boundary, and color—and demonstrated an aspect for each characteristic. Analysis on categorized traits interpreted stress responses in morphological and physiological changes. The evaluation of drought stress regardless of varieties was possible by combining various image-based traits. We might suggest that a combination of image-based traits obtained using computer vision can be more efficient than using only one trait for the precision agriculture.