Research On Optimization Of Greenhouse Intelligent Control System Based On WEB Technology

In order to improve the management efficiency of the intelligent greenhouse control system,further enhance the security and reliability of its data,and ensure that the system can perform more timely and accurate intelligent control of temperature,humidity,etc.on the greenhouse,thereby improving the yield and quality of crops.This paper studies the design and optimization of intelligent greenhouse control system based on WEB,and uses particle swarm optimization BP neural network algorithm to improve the performance of the system.The intelligent greenhouse control system uses modern information technology and intelligent control technology,which can remotely monitor and control the greenhouse environment,automatically adjust environmental factors such as temperature,humidity,and lighting,and can be monitored and controlled in real-time through remote means.In the experiment,we compared the actual planting results of the greenhouse intelligent control system optimized by the standard neural network algorithm and the intelligent control system optimized by the particle swarm optimization BP neural network algorithm.The results show that the optimized system has significantly improved compared to the unoptimized standard neural network algorithm greenhouse intelligent control system in terms of yield,individual plant yield,and fruit size.By comparing the yield and nutritional composition of strawberries before and after optimization of the intelligent greenhouse control system,the results showed that the optimized intelligent greenhouse control system had better results than the unoptimized system.Under the optimized intelligent greenhouse control system,the unit yield of strawberries increased by 14.3%,the yield per plant increased by 13.2%,and the average fruit size increased by 15.0%.This proves the effectiveness of using particle swarm optimization BP neural network algorithm to optimize the intelligent greenhouse control system.The research shows that the stability and production efficiency of the greenhouse intelligent control system have been improved to a certain extent by optimizing the greenhouse intelligent control system based on particle swarm optimization BP neural network algorithm.At the same time,remote monitoring and control can reduce labor and material costs,bringing better benefits to growers.In addition,the optimized system can also improve the yield and quality of crops planted,further promoting the sustainable development of agricultural production.