Image Processing and Roughness Analysis as a Tool for Quantification of Physiological Well-Being in plants: Results for Sunagoke Moss

Abstract

The general appearance of a plant is the most obvious indicator of its physiological well-being. This study was premised on the assumption that image roughness values can be used to quantify wellbeing in plants. We hypothesize that the highest level of well-being in the plant corresponds to a given minimum level of its surface roughness. Beyond this point the roughness increases. A set of 511 images of Sunagoke moss (Rhacomitrium canescens) samples at water states of, 5.0gg−1, 4gg−1, 3gg−1, 2.0gg−1, 1.0gg−1 and 0gg−1 were analyzed for roughness parameters. Water state here was defined as the amount of water available for the plant at the beginning of a given day in grams per gram of its dry weight. The results demonstrated that different water states have a strong effect on the surface roughness in Sunagoke moss. It was found that the higher the surface roughness of a plant the lower the level of its well-being and viceversa. The highest level of well-being was found to be at 2gg−1 water state for the Sunagoke moss used in this study. We concluded that roughness analysis can be used to quantify well-being in plants. Based on the results of this study, we propose a speaking organism system concept which allows plants to self-regulate their own bio-production environment based on roughness parameters fused with other image analysis results.