The growing world population has diversified food production scenario over the last decades. In developing countries, large area of land is not in use for crop production. Since the available land cannot be increased, tunnel farming has been used as a solution to make efficient use of land in hands. Tunnel farming cultivation is an encouraging industry of modern agriculture in the world. There are several advantages of tunnel farming, but most prominent are: (1) growing off-season vegetable or prolonging growing season, and (2) conserving crop quality and increasing crop yield on a limited land in minimum possible time. Fundamentally, there are three types of tunnel—high tunnels, walk-in tunnels, and low tunnels. The priority of tunnel farming is to control climate according to the crop need, because each crop needs its own favorable climate condition for the optimal growth. The environmental conditions affect the productivity and profitability of the crops. Nowadays computer based control systems are required for controlling climate, because manually controlling climate poses many challenges. The development of automation system for tunnels comprises different steps, including factors affecting climate, crop demands, and control algorithms. Fuzzy logic is an efficient substitute for designing a large variety of control applications. It provides an expedient method for the design of complicated dynamic system. As a result, it gives results faster than the conventional control design strategies. The beauty of fuzzy control system is that, these systems deals with nonlinear complicated mathematical models in a very simple and easy way. Its structure is fusion of control rules described by linguistic terms and defined from knowledge of process. However, a controller designed using fuzzy logic is advantageous for adapting control parameters to improve the control process. Our work 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.