Water Irrigation Control for Sunagoke Moss Using Intelligent Image Analysis

A novel technique suitable for noninvasive measurements of moss water content is presented. In this paper, colour image sensing is applied for measuring moss water content. Sunagoke moss Rhacomitrium canescens has been utilized as an active greening material to mitigate the urban heat island effect. The goal of this paper is to develop an intelligent image analysis system for water irrigation optimal control in Sunagoke moss. The combination of RGB components (green:red ratio, blue index, blue value and green index) using statistical pattern recognition can estimate water content and define the distribution of water condition in every pixel of Sunagoke moss images. The combination of colour image sensing and Artificial Neural Network (ANN) successfully described the relationship between water content and colour features i.e. average green index, average blue index, blue mean value, browning area index, green canopy index and average green:red ratio. This system is helpful to explore a new way of water spraying in Sunagoke moss plant factories based on computer vision. We propose a water irrigation technology of plant factory to realize the automation and precision farming. Precision water spraying system based on computer vision is important, not only for spraying the water scientifically, but also for improving the efficiency of spraying and decreasing the nonor off-target spraying to prevent over watering.