Intelligent irrigation control is a core technology for achieving refined management in smart irrigation engineering. Its main goal is to realize water saving and yield increase through precise water regulation. However, traditional methods have limitations such as poor environmental adaptability, large prediction deviation, overreliance on expert experience, and low water use efficiency. This paper proposes an intelligent irrigation control model that integrates neural network, Fuzzy Control, real-time irrigation forecasting, and canal system dynamic water distribution optimization model into smart irrigation engineering to achieve precise irrigation decision-making driven by multi-source data. Experiments with a self-collected multimodal irrigation dataset in facility agriculture demonstrate that the control accuracy of irrigation facilities reaches 98.65%, water use efficiency reaches 91.23%, and the water demand prediction error is only 4.32%. These results indicate that the proposed model has excellent accuracy and applicability in the field of intelligent irrigation control. It effectively solves the problems of low control accuracy and serious resource waste, provides new ideas for water resource management in smart agriculture, and contributes to the development of modern agriculture toward intelligence and efficiency.
舒慧芳 et al. (Thu,) studied this question.