Paper Title
Assessment of Water Deficit Stress in Crop using Hyperspectral Reflectance based Vegetative Indices

Abstract
In this study, performance of hyperspectral reflectance based vegetative indices was evaluated using relative water content (RWC) in plants for assessment of water deficit stress in crop. Experiment was carried out on ten rice genotypes. Five of them were drought tolerant varieties and remaining five were drought resistant verities. The hyperspectral reflectance of these genotypes were recorded and synchronized with spectral measurements, RWC values were computed using plant leaves. The regression based modeling approach was used to establish relationship between indices and RWC. The model developed using Maximum Difference Water Index (MDWI) was observed as the best predictive model. This model was having the lowest RMSEP (5.23), the highest RPD (5.49) as well as the highest R sq values for both model calibration and validation (0.92). This type of studies positively contributes for timely detection of water deficit stress in crops and provides a valuable input to the farmers and policy makers so that well in advance and effective steps can be taken to prevent loss to crop yield.