抄録
Mass production of moss plant has been expected because of huge demand of roof top greening of factory buildings. A biotechnology for proliferation of sunagoke moss has been developed. It will be produced from plant factory. A bio-response feedback control strategy known as Speaking Plant Approach (SPA) was applied to the automated moss plant production system. Moisture content, water potential and leaf area index were measured and used for an Artificial Neural Network (ANN) model output. Three textural analysis features (Energy, Local homogeneity, Contrast) were obtained for input parameters of the model. The results of the experiment using ANN model show that it is possible to predict the moss water status parameters by using textural features. It was shown that through appropriate selection of the architecture of the network, all parameters of moss water status can be predicted. By using back-propagation supervised learning and inspection data method, ANN prediction model was tested successfully describing the relationship between textural features and water status parameters. It also produced high correlation between measured and predicted value (R2 ranged from 0.90 to 0.98) and minimum absolute error using inspection data. This indicates that SPA will become an attractive strategy for control system for moss production factory.
