Comparison of Plant Water Stress Detection Ability of Color and Gray-Level Texture in Sunagoke Moss

Abstract

Imaging is emerging as a novel technique for real-time and non-invasive stress diagnosis in plants. In this study, water stress detection ability of six texture features extracted using color co-occurrence matrix (CCM) and gray-level co-occurrence matrix (GLCM) methods were compared. Multi-layer perceptron neural network (MLPNN) models were developed and used to predict or classify water stress in five samples of Sunagoke moss (Rhacomitrium canescens) using the texture features. Hue-saturation-intensity (HSI) texture features had an average water stress prediction mean square error (MSE) of 0.0043. The average water stress prediction MSE of GLCM texture features was 0.0053. The CCM and GLCM texture features showed average water stress misclassification errors of 3.05% and 1.78%, respectively. Texture features extracted from HSI color space showed a higher ability and reliability to predict and classify water stress in the plant. By discarding the intensity component, the HSI texture features can be used to detect water stress in Sunagoke moss under natural lighting conditions. Use of hyperspectral reflectance imaging can enable this method to detect specific stress signatures for particular plants and make it a powerful biophysical, non-invasive, and pre-visual stress diagnosis method for a wide variety of stresses in plants.