Determining an ANN pre-treatment algorithm to predict water content of moss using RGB intensities

Sunagoke moss is one of the plant products that are cultivated in a plant factory. One of the primary determinants of moss growth is water availability. The present work attempts to apply precision irrigation system using machine vision in plant factories. The specific objective was to evaluate the ability of bio-inspired approaches as pre-treatment algorithm of Artificial Neural Network (ANN) for determining water content of moss. The results showed that ANN was capable for predicting water content of moss using RGB intensities, and then some bio-inspired approaches such as Honey Bees Mating Optimization (HBMO), Ant Colony Optimization (ACO), Genetic Algorithms (GAs), Simulated Annealing (SA) and Discrete Particle Swarm Optimization (DPSO) were capable of optimizing the feature selection process.