Mohamed Habib Sellami; Farhat Sahbani
Abstract
Farmers and experts are continuously searching for optimal conditions to improve the productivity of tomatoes in greenhouses. To provide an answer to their concerns, we have developed a modeling approach to automate the functioning of a greenhouse cultivated with tomato plants. For the aeration, heating, ...
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Farmers and experts are continuously searching for optimal conditions to improve the productivity of tomatoes in greenhouses. To provide an answer to their concerns, we have developed a modeling approach to automate the functioning of a greenhouse cultivated with tomato plants. For the aeration, heating, and irrigation systems, we compared the Proportional Integral Derivative (PID) controller response to the Fuzzy logic (FL) controller response. For the aeration system, the response of both controllers was stable, with a pick of about 1.09 for the PID controller and zero for the fuzzy controller. Likewise, there was no overtaking for the fuzzy controller but about 8.28% for the PID controller. The rise time for the fuzzy controller was less than that of the PID controller (627 s). We signaled a stable response for the PID controller and the fuzzy logic controller for the irrigation system. The pick and the overtake were equal to zero for the fuzzy logic controller but were 1.28 and 28.2 s for the PID controller, respectively. In the case of both controllers, the rise time was the same, equaling 18.3 s. The regulation time was less than 35 s for the fuzzy logic controller and 31.1 s for the PID controller.
