Neural-Genetic Algorithm as Feature Selection Technique for Determining Sunagoke Moss Water Content

This study investigated the use of machine vision for monitoring water content in Sunagoke moss. The main goal is to predict water content by utilizing machine vision as non-destructive sensing and Neural-Genetic Algorithm as feature selection techniques. Features extracted consisted of 13 colour features, 90 textural features and three morphological features. The specificities of this study was that we were not looking for single feature but several associations of features that may be involved in determining water content of Sunagoke moss. The genetic algorithms successfully managed to select relevant features and the artificial neural network was able to predict water content according to the selected features. We propose neural network based precision irrigation system utilizing this technique for Sunagoke moss production.