Greenhouse Climate Modeling Using Fuzzy Neural Network Machine Learning Technique

The greenhouse climate is a non-linear system that contains multiple inputs (predictors) and outputs (responses). This project aimed to provide solution, aided by artificial intelligence, the issue of variations in time, input output factors internal can adversely affect tomato seedlings. Machine learning Methodologies such as fuzzy inference neural networks have been applied mimic idealistic behavior. paper proposes an adaptive based on technique embedded with logic calls Adaptive Neuro Fuzzy Inference System (ANFIS) predict air humidity, temperature, radiation, CO2 concentration while seeds grow, order produce favorable conditions. parameters include ten meteorological control actuators majorly influence plants during their growth process climate. discussion revolves around linguistic ANFIS model will operate 48 days it takes for seedlings grow. It estimates using data along rooted trained back propagation optimization algorithm, 500 iterations least square algorithm. Simulations revealed efficiency this model.