Abstract
An agricultural greenhouse is an environment to ensure intensive agricultural production. The favorable climatological conditions (temperature, lighting, humidity ...) for agricultural production must be reproduced in a non-natural way by controlling these parameters using several actuators (heating/air conditioning, ventilation, and humidifier/ dehumidifier). The objective of this study is to control the humidity inside the greenhouse; it is a problem that remains to be negotiated. To that end, an actuator based on a humidifier and a dehumidifier was installed in an experimental greenhouse and activated by a fuzzy logic controller to achieve the desired optimal indoor humidity in the greenhouse.
Highlights
A greenhouse is intended to protect plants and promote greenhouse production by creating climatic conditions that are more favorable than the local climate
An actuator based on a humidifier and a dehumidifier was installed in an experimental greenhouse and activated by a fuzzy logic controller to achieve the desired optimal indoor humidity in the greenhouse
Many researchers have developed several control strategies to improve the indoor microclimate such as Proportional - Integral Derivative controller (PID controller) [1], Neural Network [2,3], the PI controller (SSODPI and PI-SSOD event controllers) [4], Adaptive Neuro-fuzzy controller [5,6,7,8], Genetic algorithm [9], Optimal control [10], Predictive Neural Control [11], four control techniques have been developed [12]: Adaptive Neuro-Fuzzy Control (ANFIS), Fuzzy Logic Control (FLC), PI Control and Artificial Neural Network Control (ANN), to adjust the temperature inside the greenhouse, and a Fuzzy Logic Controller (FLC) [13,14] which is a valuable element in the control of hardly identifiable and non-linear systems
Summary
A greenhouse is intended to protect plants and promote greenhouse production by creating climatic conditions that are more favorable than the local climate. Many researchers have developed several control strategies to improve the indoor microclimate such as Proportional - Integral Derivative controller (PID controller) [1], Neural Network [2,3], the PI controller (SSODPI and PI-SSOD event controllers) [4], Adaptive Neuro-fuzzy controller [5,6,7,8], Genetic algorithm [9], Optimal control [10], Predictive Neural Control [11], four control techniques have been developed [12]: Adaptive Neuro-Fuzzy Control (ANFIS), Fuzzy Logic Control (FLC), PI Control and Artificial Neural Network Control (ANN), to adjust the temperature inside the greenhouse, and a Fuzzy Logic Controller (FLC) [13,14] which is a valuable element in the control of hardly identifiable and non-linear systems. The author in [16] have developed a fuzzy modeling application to control the indoor air temperature of a MISO greenhouse, [17] have used this application with a new approach that automatically organizes a fuzzy flat system into a hierarchical collaborative architecture, this architecture adapted to transfer the information contained in the fuzzy rule sets to another fuzzy subsystem
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