Varietal discrimination of common dry bean (Phaseolus vulgarisL.) grown under different watering regimes using multitemporal hyperspectral data

Abstract

Globally, the common dry bean varieties (Phaseolus vulgaris L.) are regarded as valuable food crops. Due to diversion-farm and postharvest management requirements, quick, reliable, and cost-effective varietal discrimination is critical for optimal management during growth and after harvesting. The large number of valuable wavelengths that characterize hyperspectral remotely sensed datasets in concert with emerging robust discriminant analysis techniques offers great potential for on-farm dry bean varietal discrimination. In this study, an integrated approach of partial least-squares discriminant analysis (PLS-DA) on hyperspectral data was used to determine the bean’s optimal timing for on-farm varietal discrimination. Based on experimental plots underirrigated and rain-fed watering regimes, hyperspectral data were collected at three major phenological stages. Data at each stage were first used to generate PLS-DA models to determine variable (wavebands) importance in the projection (VIP) and the VIP bands used to generate VIP conditioned PLS-DA models. The study identified 6 weeks (branching and rapid vegetative growth) and 10 weeks (flowering and pod development) after seed sowing as optimal stages for varietal discrimination. The study offers insight into the optimal period to discriminate dry bean varieties using spectroscopy, valuable for on-farm and after-farm management and crop monitoring sensor development.