Using red–green–blue vegetation indices to evaluate complex agronomical traits in soybean breeding

Abstract

High-throughput phenotyping is an emerging tool that allows access to identify simple and complex traits, accelerating genetic discoveries and selection. Vegetation indices strongly correlate with several economic crop traits, allowing plant breeders to detect variation in breeding populations. Thus, this study used red–green–blue (RGB) vegetation indices to evaluate the influence of the stink bug complex (Euschistus heros, Piezodorus guildinii, Nezara viridula, Dichelops melacanthus, and Edessa meditabunda) on the agronomical traits of soybean (Glycine max) lineages. For instance, two experiments were conducted to assess soybean resistance to the stink bug complex, (1) with and (2) without pesticide control. An unmanned aerial vehicle coupled with an RGB camera acquired aerial photography over the field during the R5 stage. Four vegetation indices and canopy were estimated from the orthomosaic, and the genotypes were evaluated based on agronomical traits. Linear mixed models were used to estimate the variance and significance test of each trait using the likelihood ratio test, and the principal component analysis was performed to verify the multivariate pattern among genotypes. The results showed significant genotypic effects for most traits with high broad-sense heritability for agronomical traits and moderate for vegetation indices. Significant correlations using best linear unbiased predictions were observed among the agronomical traits with the vegetation indices and canopy coverage, which can be used as a tool for the indirect selection of soybean lineages in the breeding pipeline.