Development and Evaluation of A Comprehensive Greenhouse Climate Control System Using Artificial Neural Network

Development of controlled environment in greenhouse is of prim importance for out of season production, increasing yield and enhancing the quality of produce. Due to high cost and impossibility of continuous human attendance in greenhouse, it is desirable to control the greenhouse environment by employing automatic control devices. In This study, the greenhouse conditions were controlled by using artificial neural network (ANN). First, an experimental greenhouse was built and equipped with control instruments. Then by using electronic sensors, some climatic parameter data (temperature, humidity, carbon dioxide and light index) were measured and saved during five minute periods. In the next stage, three types of ANN including feed forward neural networks with multiple delays in the input, two-layer neural network with a feedback from hidden layer and input delay and three-layer neural network with two feedbacks from hidden layers and input delay were trained by 66% of the recorded data, and were evaluated by using the remaining data. The three-layer neural network with two feedbacks from hidden layers and input delay was able to better predict humidity and light index of the greenhouse with MSE,s of 0.025 and 0.032, respectively. Temperature and infrared index were better predicted by using the feed forward neural networks with multiple delays in the input with MSE,s of 0.016 and 0.017, respectively. In all cases, training time was less than 14 minutes and simulation time being always less than 0.2 second, makes using neural network feasible for automatic control of greenhouse.