Research progress on rapid detection technology of soybean phenotypic indicators under saline-alkali stress

The progress of soybean phenotypic detection and intelligent sensing technologies has been reviewed under salt-alkali stress , and an integrated approach combining three-dimensional imaging with near-infrared spectroscopy has been proposed to construct full-spectrum three-dimensional images. The approach could provide a reference for the breeding of salt-alkali-tolerant soybean varieties and the optimization of cultivation practices. Soil saline-alkali is one of the major environmental factors limiting global agricultural development, posing a serious challenge to normal crop growth, resource use efficiency, and sustainable agricultural development. Soybeans are a vital oilseed crop and plant-based protein source, and their phenotypic traits are significantly affected by saline-alkali stress, severely limiting soybean grain yield and quality. With the rapid advancement of technologies such as intelligent sensing and big data, this progress has driven new developments in plant phenomics detection, offering fresh insights into germplasm resource evaluation, breeding, gene function, and the cultivation of salt-alkali stressed soybeans. This article introduces the impact of salinity-alkali stress on soybean "phenotype-environment-gene" information, reviews the technical progress and application fields of traditional phenotypic detection methods for obtaining various phenotypic indicators across crops, and focuses on a rapid detection method of soybean phenotype under salinity-alkali stress. This paper analyzes the current state of research on detecting phenotypic indicators of soybeans under saline-alkali stress using intelligent sensing methods, including near-infrared spectroscopy, image recognition, and three-dimensional imaging. It is anticipated that through the integration of three-dimensional imaging and near-infrared spectroscopy, forming "full-spectrum three-dimensional images" with spatial structure and spectral information, this approach will advance the breeding and cultivation of superior salt-alkali tolerant soybean varieties through "intelligent data-driven" methods.