Fuzzy Climate Decision Support Systems for Tomatoes in High Tunnels

Abstract

This paper presents a novel climate decision support system for tomatoes in high tunnels using fuzzy logic and adaptive neuro-fuzzy inference system. Three climate decision support systems are developed for high tunnels using fuzzy logic. First climate decision support system takes five inputs—temperature, relative humidity, solar radiations, wind velocity, and weather condition—and controls four outputs—tunnel’s temperature, tunnel’s humidity, fan speed, and shading. Second climate decision support system takes three inputs—temperature, solar radiations, and weather condition—and controls artificial sunlight. Third climate decision support system takes air quality index and controls air purification. We develop and implement the two main algorithms for climate control systems, one algorithm is for fuzzy logic climate decision support system, and other one is for neuro-fuzzy climate control system. We compute time complexity of both algorithms. We use software MATLAB for showing average error between calculated and targeted outputs. We also perform optimization of fuzzy membership functions using particle swarm optimization method and evaluate its results in MATLAB. Our generated results are very much precise and satisfied the desired range of outputs.