Use of leaf colour for drought stress analysis in rice

We propose a novel approach of utilizing phenomic traits to automatically quantify stress in plants using machine learning techniques. Moisture deficit conditions cause change in leaf color due to decrease in chlorophyll content as chloroplast is damaged by active oxygen species. Therefore, the proposed technique uses leaf color as the phenomic trait to assess stress levels using Relative water content (RWC) as a quantitative proxy. We extracted the change in leaf color in response to drought stress using the color features obtained using Random forest. A regressor has been modeled to predict the stress level of rice genotypes via RWC by employing colour histogram as a feature vector. The experiment was performed with pot images of different rice genotypes under normal and drought stressed conditions. We report a correlation coefficient of 0.89 obtained using this model demonstrating the capability of the presented technique for stress level predictions.