Automatic water-saving irrigation technology for farmland based on PSO-ELM algorithm and micro control unit

In response to the significant waste of agricultural irrigation resources and the inaccuracy of water demand predictions, this study aims to develop an automated irrigation system that can reduce fluctuations in water volume and enable precise control. Against the backdrop of current water scarcity and low agricultural water efficiency, improving irrigation precision is of great significance for ensuring food security and promoting sustainable agricultural development. This study combines particle swarm optimization algorithm with extreme learning machine and integrates it into a microcontroller to construct a new intelligent irrigation system. This technology can solve the problem of inaccurate crop water demand predictions in existing technologies and promote the transformation of intelligent agriculture from empirical to data-driven. This technology uses a LoRa based wireless sensor network to collect data and is controlled by a microcontroller. The particle swarm algorithm optimizes the initial parameters of the extreme learning machine, improving the accuracy with which it predicts farmland water demand. The results showed that the proposed method had the lowest root mean square error value, with an average of only 0.1025, indicating that the algorithm had the most accurate irrigation prediction effect. The automatic water-saving irrigation technology proposed in this study required less water compared to traditional irrigation techniques, with a minimum water consumption of 3015 m 3 /hm 2 and a maximum water consumption of only 5268.3 m 3 /hm 2 . The system’s accuracy in predicting crop irrigation water demand could reach over 98%. The method proposed in this study can accurately control irrigation water. It can also maximize irrigation water conservation. This brings new research directions for the knowledge system of automated water-saving irrigation technology in farmland. It also provides new technical ideas for the development of intelligent agricultural irrigation technology.