Prediction of Chlorophyll Content of Tomato Plant by Artificial Neural Networks and Adaptive Nero-Fuzzy Inference System

AbstractApproximately three-quarters of harvested tomatoes are freshly used. Good quality is an important factor in distributing of fresh tomato. Chlorophyll is the green chemicals to provide required food of plants and ensure plant growth and productivity. The main function of chlorophyll is to absorb blue and red lights and perform photosynthesis. In recent years, the tendency to use of prediction methods such as soft computing and artificial intelligence for growth of plans has increased. The main aim of this study was to investigate the relationship between height and chlorophyll content in the leaves of tomato plants using modeling and predicting techniques and compare the accuracy of these methods. In this study, some cultivated plants of tomato were randomly selected for height and SPAD measurements. The results showed the relationship between Chlorophyll content and height of plants was very low (R۲ = ۰.۲۷۶). However using the modelling of ANN and ANFIS improved the prediction power up to (R۲=۰.۹۸۲ and ۰.۹۱۳), respectively.

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Keywords: ANFIS ، Chlorophyll Content ، Modeling ، Neural Networks ، Tomato