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In a plant factory, optimal control for obtaining higher yield, higher production efficiency, minimum waste, and better quality of plants is essential. Sunagoke moss is one of the plant products which are cultivated in plant factory. One of the primary determinants of moss growth is water availability. Hence, there is need to develop precision irrigation for moss production in plant factory. The present work attempted to develop machine vision-based micro-precision irrigation system to optimize water use in plant factory and maintain the water content of moss constantly in optimum growth condition. The specific objective of this study is to propose nature-inspired algorithms to find the most significant set of image features suitable for predicting water content of cultured Sunagoke moss. Feature Selection (FS) methods include Neural-Genetic Algorithms (N-GAs) and Neural-Discrete Particle Swarm Optimization (N-DPSO), Neural-Honey Bee Mating Optimization (N-HBMO) and Neural-Fish Swarm Intelligent (N-FSI). Image features consist of color features and textural features with the total of 212 features extracted from grey, RGB, HSV, HSL, L*a*b*, XYZ, LCH and Luv color spaces. Back-Propagation Neural Network (BPNN) model performance was tested successfully to describe the relationship between water content of Sunagoke moss and image features. FS methods improve the prediction performance of BPNN.