Greenhouse environmental monitoring and control system based on improved fuzzy PID and neural network algorithms

Abstract

Compared with traditional agriculture, greenhouse planting can more accurately control the growth environment, thereby improving the yield and agricultural product quality. However, traditional greenhouse environments (GhEs) present a number of challenges, including inflexibility in monitoring and wiring, difficulty in management, and high labor costs. To improve the limitations of traditional GhEs and enhance the accuracy of GhE monitoring and control systems, a sensor-based GhE monitoring and control system is designed. In addition, a prediction model for GhE monitoring is constructed using a backpropagation neural network to better predict nonlinear factors such as humidity, temperature, and light intensity in the GhE. Simultaneously, an improved fuzzy proportional integral derivative (PID) controller is utilized to address issues such as fuzziness and uncertainty in GhEs. The results show that the temperature error of the greenhouse environment monitoring and control system based on improved fuzzy PID and neural network algorithms is 0.35-5.04%, the humidity error is -1.3 to 1.65%, and the lighting error is -3.5 to -0.79%. Comprehensive data show that the greenhouse environmental monitoring and control (GEMC) system based on improved fuzzy PID and neural network algorithms effectively improves the accuracy of environmental monitoring and control. The GEMC system, which is based on improved fuzzy PID and neural network algorithms, has facilitated the advancement of agricultural technology in China. It has also provided support and a reference point for GhE monitoring and agricultural production.