Intelligent Irrigation Control Using Color, Morphological and Textural Features in Sunagoke Moss

Abstract

A non-invasive sensing technique for monitoring Sunagoke moss water conditions was proposed. This paper describes the design and development of precision irrigation control method by incorporating color, morphology and RGB color co-occurrence matrix (CCM) textural features. The objective of this study was to develop a model of artificial neural network and made comparison analysis of the color, morphology and textural features to determine appropriate combination of pictorial features to accurately predict water content. Optimum condition of Sunagoke moss based on photosynthesis rate, color features, morphological features and textural features can be achieved between 2 gg-1– 2.5 gg-1 water content. Neural network model performance was tested successfully to describe the relationship between water content and image features (color, morphology and textural features). This system is helpful to explore the new way of water spraying in moss plant factories based on computer vision. It proposes the water irrigation technology of the plant factory to realize the automation and precision farming. Precision water and nutrition spraying system based on computer vision is very important, not only for spraying the water and nutrition scientifically, but also for improving the efficiency of spraying and decreasing the non- or off-target of moss to prevent from over watering.