Bio-inspired feature selection to select informative image features for determining water content of cultured Sunagoke moss

One of the primary determinants of Sunagoke moss Rachomitrium japonicum growth is water availability. There is need to develop a non-destructive method for sensing water content of cultured Sunagoke moss to realize automation and precision irrigation in a close bio-production systems. Machine vision can be utilized as non-destructive sensing to recognize changes in some kind of features that describe the water conditions from the appearance of wilting Sunagoke moss. The goal of this study is to propose and investigate bio-inspired algorithms i.e. Neural-Ant Colony Optimization, Neural-Genetic Algorithms, Neural-Simulated Annealing and Neural-Discrete Particle Swarm Optimization to find the most significant sets of image features suitable for predicting water content of cultured Sunagoke moss. Image features consist of 8 colour features, three morphological features and 90 textural features (RGB, HSV, HSL colour co-occurrence matrix and gray level co-occurrence matrix textural features). Each colour space of textural features consist of energy, entropy, contrast, homogeneity, inverse difference moment, correlation, sum mean, variance, cluster tendency and maximum probability. The specificity of this problem is that we are not looking for single image feature but several associations of image features that may be involved in determining water content of cultured Sunagoke moss. All feature selection models showed that prediction performance is getting better through all the iterations. It indicates that all models are effective. Neural-Ant Colony Optimization had the best performance as a feature selection technique. The minimum average prediction mean square error (MSE) achieved was 1.75 × 10−3. There is significant improvement between method using feature selection and method without feature selection.