Assessing genetic diversity of soybean based on smartphone image-derived canopy parameter

Abstract

Abstract Convenient and accurate characterization of field-grown crops is an urgent need not only for effective use of germplasm resources but also further for breeding programs. In this study, smartphone image-derived canopy parameter was utilized to study the genetic relationships among 18 soybean accessions at early growing period. Field experiments were conducted in two consecutive years (2021 and 2022). Canopy cover rate (CCR) per plant that is canopy parameter and relative indicator were estimated using image analysis software from top-view image without any significant alteration of plant morphology. CCR can be used as the indirect and comprehensive index for investigating morphological and biomass traits at early growing period in field-grown soybean accessions. Principal component analysis (PCA), hierarchical cluster analysis (HCA) and discriminant analysis (DA) were performed for grouping soybean accessions. For DA, average of percentage of correctly classified on the basis of CCR was 88.9% of grouped cases by PCA. Results of HCA based on CCR were more similar to the ones suggested by the PCA than ones of clustering based on morphological and biomass traits. Overall results suggest that CCR can be used as the useful index for diversity in field-grown crop genotypes. This study provides not only an innovative method for comprehensive evaluation of crop genetic resources using image-derived parameter, but also an approach to promote genetic improvement of crop varieties.