Design of Plant Growth Lamp Control System Based on Convolutional Neural Network and Fuzzy Control

Currently, Plant growth lights require manual setting of the light intensity based on the growth status of plants, combined with work experience, to meet the growth needs of plants.This method incurs high labor costs and low operational efficiency. At the same time, the lighting environment is nonlinear, with many uncertain factors,leading to relatively low control accuracy. In response to these issues, a plant growth lamp control system has been designed that integrates convolutional neural networks and fuzzy control algorithms. The system collects data on plant growth conditions and environmental parameters using cameras and light intensity sensors. It employs a convolutional neural network model to automatically detect the growth status of the plants. Upon detecting the current state, it retrieves the corresponding optimal light intensity requirements from a data table. Subsequently, a fuzzy controller adjusts the light intensity of the plant growth lamps autonomously, thus achieving a high level of automation and improved control precision. By implementing precise control, this system aims to enhance both the quality and yield of the plants.