Application of sensor based high-throughput phenotyping methods to study drought stress in soybean

Abstract

Drought is crucial threat worldwide for crop production, especially present rapid climate changing situation. Current drought solutions: improving irrigation system, rainwater harvesting, damming, cloud seeding, and some changes of cultivation methods, although they are effective each has their economic, environmental, and temporal drawbacks. Among all solutions, the most effective, inexpensive and manageable method is the use of drought resistance cultivars, via plant breeding. However, conventional plant breeding is a time-consuming and laborious task especially for the phenotypic data acquisition of the targeting traits of numerous progenies. The recently emerged method, high-throughput phenotyping (HTP), has potential to overcome the foresaid issues. Its massive, accurate, rapid, and automatic data acquisition in breeding procedure can be the breakthrough for developing drought resistant/tolerant cultivar to solve current drought problems. Thus, the current article will introduce various methods of HTP to detect drought stress, which can accelerate the drought resistance cultivar breeding processes in order to provide helpful guidelines for to choose the appropriate methods for breeders and researchers under their circumstances.

The steep increase of drought frequency under global warming has resulted in massive losses to world crop production. Consequently, drought-tolerant cultivars are required to overcome this crisis under the given circumstances. In order to develop new drought-tolerant cultivars efficiently, it is crucial to phenotype massive numbers of individuals in a fast, reliable, and precise manner, which has led to the advent of high throughput phenotyping. In this report, we demonstrate fast and reliable phenotyping methods to screen drought tolerance in soybeans (Glycine max L.). Recent studies have revealed that biomass and yield are positively correlated with the number of nodes and canopy/green area. The results showed that green pixel percentage has a significant correlation with the number of main nodes. This case study demonstrates that the green pixel percentages would be useful for drought evaluations in further experiments.