Classification of Water Stress in Sunagoke Moss Using Color Texture and Neural Networks
The general appearance of a plant is the most obvious indicator of its physiological well- being. Color Co-occurrence Matrix (CCM) texture features, extracted from a set of 1095 images were used to classify water stress in Sunagoke moss (<I>Rhacomitrium canescens</I>) using Neural Networks (NN). An Excess Green Water Stress Index (EGWSI) was developed and used to quantify water stress in the sample. The CCM texture features were extracted from: red-green-blue (RGB); hue-saturation-intensity (HSI) and CIE's (Comission Internationale de LEclairage) LAB and XYZ color spaces. The HSI texture features achieved 99.45% water stress classification efficiency. They were followed by RGB, XYZ and LAB texture features with classification efficiencies of 99.07%, 98.83 and 96.3% in that order respectfully. The HSI textures features displayed a higher ability and reliability to classify water stress in Sunagoke moss and can be used for stress detection under varying light intensities. A significant accomplishment of this study was the detection of both flood and draught water stress in a plant that exhibits a high level of desiccation tolerance. This provides an opportunity for the possibility of allowing plants to control their own bioproduction environments.
- Environment control in biology
Environment control in biology 46(1), 21-29, 2008-03-30
Japanese Society of Agricultural, Biological and Environmental Engineers and Scientists