Low-Cost Hyperspectral Imaging to Detect Drought Stress in High-Throughput Phenotyping

Recent developments in low-cost imaging hyperspectral cameras have opened up newpossibilities for high-throughput phenotyping (HTP), allowing for high-resolution spectral data to beobtained in the visible and near-infrared spectral range. This study presents, for the first time, theintegration of a low-cost hyperspectral camera Senop HSC-2 into an HTP platform to evaluate thedrought stress resistance and physiological response of four tomato genotypes (770P, 990P, Red Setterand Torremaggiore) during two cycles of well-watered and deficit irrigation. Over 120 gigabytesof hyperspectral data were collected, and an innovative segmentation method able to reduce thehyperspectral dataset by 85.5% was developed and applied. A hyperspectral index (H-index) basedon the red-edge slope was selected, and its ability to discriminate stress conditions was comparedwith three optical indices (OIs) obtained by the HTP platform. The analysis of variance (ANOVA)applied to the OIs and H-index revealed the better capacity of the H-index to describe the dynamicof drought stress trend compared to OIs, especially in the first stress and recovery phases. SelectedOIs were instead capable of describing structural changes during plant growth. Finally, the OIs andH-index results have revealed a higher susceptibility to drought stress in 770P and 990P than RedSetter and Torremaggiore genotypes.