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

Recent developments in low-cost imaging hyperspectral cameras have opened up new possibilities for high-throughput phenotyping (HTP), allowing for high-resolution spectral data to be obtained in the visible and near-infrared spectral range. This study presents, for the first time, the integration of a low-cost hyperspectral camera Senop HSC-2 into an HTP platform to evaluate the drought stress resistance and physiological response of four tomato genotypes (770P, 990P, Red Setter and Torremaggiore) during two cycles of well-watered and deficit irrigation. Over 120 gigabytes of hyperspectral data were collected, and an innovative segmentation method able to reduce the hyperspectral dataset by 85.5% was developed and applied. A hyperspectral index (H-index) based on the red-edge slope was selected, and its ability to discriminate stress conditions was compared with 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 dynamic of drought stress trend compared to OIs, especially in the first stress and recovery phases. Selected OIs were instead capable of describing structural changes during plant growth. Finally, the OIs and H-index results have revealed a higher susceptibility to drought stress in 770P and 990P than Red Setter and Torremaggiore genotypes.

Automated summary

Recent developments in low-cost imaging hyperspectral cameras have opened up new possibilities for high-throughput phenotyping. This study integrated a low-cost hyperspectral camera into an HTP platform to evaluate the drought stress resistance and physiological response of four tomato genotypes. The study collected over 120 gigabytes of hyperspectral data and developed an innovative segmentation method to reduce the dataset. The results showed the better capacity of the hyperspectral index to describe the dynamic of drought stress trend compared to optical indices, while the selected optical indices were capable of describing structural changes during plant growth.