Universal Journal of Control and Automation 1(1): 10-14, 2013 http://www.hrpub.org DOI: 10.13189/ujca.2013.010102 Development and Evaluation of A Comprehensive Greenhouse Climate Control System Using Artificial Neural Network Mohsen Alipour, Mohammad Loghavi * Departmet of Mechaics of Agricultural Machinery, Shiraz University, Shiraz, Iran *Corresponding Author: loghavi@shirazu.ac.ir Copyright © 2013 Horizon Research Publishing All rights reserved. Abstract 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. Keywords Greenhouse Control, Artificial Neural, Climate Control, Network 1. Introduction Greenhouse is a structure in which temperature, humidity, light and carbon dioxide are controllable. Inside greenhouses most plants can be grown throughout the year, especially out of season and some products could be prematurely ripened and kept for a full or part of a year during adverse climatic conditions [1]. The degree of thermal control depends on the type of plants that are propagated or grown [2]. Temperature affects photosynthesis, breathing, evapotranspiration, vegetative growth and leaf color of plants inside the greenhouse. Intensity, duration of radiation and quality of light is important for plants and varies depending on plant type [1]. Proper relative humidity enhances leaf vigor and freshness. High humidity along with high temperature causes propagation of fungi and other plant diseases. Controlling the concentration of carbon dioxide can increase yield [2]. ANN is a soft computing technique that by learning process and employing simple processors by the name of neurons tries to establish a correlation between input space (input layer) and the target space (output layer) by discovering the inherent relations among data. The hidden layers process the data received from input layer and produce answer in the output layer. Each network is trained by receiving some examples. Training is a process that eventually ends up to learning. Network learning is terminated when the connection weights between the layers are changed until the difference between predicted and the target (experimental) is reduced to permissible limits. The trained neural network can be used for predicting outputs relevant to a set of new data. Regarding to the structure of artificial neural network, its main characteristics include high processing rate, learning ability through pattern presentation, ability to generalize the knowledge after learning, flexibility in dealing with un-desired errors and not making any significant interruption in case of damage in any part of network connections due to distribution of the connection weights in the network [3]. Seginer et al. [4] used neural network for modeling greenhouse climate. They used experimental data from a few research greenhouses for training the neural network and found a proper approach for predicting the greenhouse inside