Intelligent Control of Agricultural Irrigation Based on Reinforcement Learning
Zhou Ni
Abstract
Abstract In the traditional agricultural irrigation control methods, flood irrigation and manual control are generally used to irrigate the land, and the effective utilization rate of water is only 20% -35%. With the advancement and development of science and technology, especially with the rapid development and application of sensor technology, wireless communication technology, reinforcement learning and deep learning technology, and intelligent terminals, intelligent control of agricultural irrigation integrating these high and new technologies has been adopted to improve water resources in agricultural irrigation. The utilization efficiency has become an inevitable trend and fundamental requirement for the development of precision agriculture and facility agriculture. This paper proposes an intelligent control method for agricultural irrigation based on reinforcement learning. By constructing a deep learning network to extract features from the raw sensor data and construct Q-learning features, using deep reinforcement learning powerful data learning capabilities, the precision of agricultural irrigation control can be effectively improved. The effectiveness of this method is verified by algorithm training and testing in a greenhouse plantation of a company in Hunan.