Evaluation of different water absorption bands, indices and multivariate models for water-deficit stress monitoring in rice using visible-near infrared spectroscopy.

Abstract

Accurate estimation of plant water status is a major factor in the decision-making process regarding general land use, crop water management and drought assessment. Visible-near infrared (VNIR) spectroscopy can provide an effective means for real-time and non-invasive monitoring of leaf water content (LWC) in crop plants. The current study aims to identify water absorption bands, indices and multivariate models for development of non-destructive water-deficit stress phenotyping protocols using VNIR spectroscopy and LWC estimated from 10 different rice genotypes. Existing spectral indices and band depths at water absorption regions were evaluated for LWC estimation. The developed models were found efficient in predicting LWC of the samples kept in the same environment with the ratio of performance to deviation (RPD) values varying from 1.49 to 3.05 and 1.66 to 2.63 for indices and band depths, respectively during validation. For identification of novel indices, ratio spectral indices (RSI) and normalised difference spectral indices (NDSI) were calculated in every possible band combination and correlated with LWC. The best spectral indices for estimating LWC of rice were RSI (R1830, R1834) and NDSI (R1830, R1834) with R2 greater than 0.90 during training and validation, respectively. Among the multivariate models, partial least squares regression (PLSR) provided the best results for prediction of LWC (RPD=6.33 and 4.06 for training and validation, respectively). The approach developed in this study will also be helpful for high-throughput water-deficit stress phenotyping of other crops.