High-throughput phenotyping for terminal drought stress in chickpea (Cicer arietinum L.)

Abstract

Most chickpea cultivation occurs in rainfed environments, where unpredictable rainfall leads to drought stress, consequently reducing growth and productivity. Fast and robust image-based screening methods would greatly facilitate drought tolerance research. In this study, an experiment was conducted in a climate-controlled environment, using radio frequency-enabled ID (RFID) tagged plant carriers on Lemnatec's high-throughput phenotyping platform. The agro-physiological characteristics of six chickpea genotypes under drought stress conditions imposed at the early podding stage were explored. Using non-destructive techniques, including Red, Green, and, Blue (RGB), Near-Infrared (NIR), Infrared (IR), and chlorophyll fluorescence (Fv/Fm) imaging, data was captured at various stages of drought stress, quantified as the fraction of transpirable soil water, using LemnaGrid software. Traits such as plant phenology, yield, yield components, and physiological parameters (e.g., leaf temperature and photosynthetic characteristics) for both drought-stressed and well-watered plants were recorded manually. Seed yields ranged from 9.9 to 18.1 g plant−1 under WW, and 2.6–13.7 g plant−1 under DS. DS decreased yield the most in ICC 1882 and RSG 888 (73.7%) and the least in ICC 4958 (24.3%) relative to WW. Our findings revealed significant genotype × water treatment interactions for all manually recorded traits. Moreover, strong positive correlations were observed between manually recorded and image-based traits, i.e., between aboveground dry weight and projected area, aboveground dry weight and convex hull area, plant height and caliper length, photosynthetic rate, and chlorophyll fluorescence, stomatal conductance and NIR reflectance, IR thermometer temperature and IR imaging temperature. Notably, the strong positive correlations between NIR reflectance and stomatal conductance, and between chlorophyll fluorescence and photosynthesis, underscore the immense potential of harnessing image-based screening methods in breeding programs to enhance drought tolerance.