| dc.description.abstract | A model of how a plant reacts to their micro climate is of great importance to control the physical micro climate around the plant and to other major input problems (such as nutrition intake). Artificial neural network (ANN) can be utilized to model a plant reaction to their microclimate in a more objective fashion by applying the ANN to measured data, and not from a pre-assumed model structure. This paper discusses a model of the plant response (the ratio of canopy area-stem diameter of baby cucumber) and the loss of nutrition solution as output and nutrition solution intake and microclimate (temperature, humidity, and irradiation) as input by using artificial neural network of dynamic response. The efficiency model (EI) and the average of percentage of deviation (APD) in training for the ratio of canopy-stem diameter was 95% ± 1.3%: and the loss of nutrition solution was 99% ± 4.9%. vVhereas, in validation, the ratio of canopy-stem diameter was 93% ± 0.62%; and the loss of nutrition solution was 96% ± 0.43%. The results showed that the ANN model of dynamic response had good agreement in predicting the plant response and the loss of nutrition solution based on nutrition solution intake and microclimate. The method of capturing of cucumber image and image processing software has been successfully developed for predicting canopy area of the plant. Keywords: image processing, plant response, nutrition solution, microclimate, artificial neural networks | id |
