My research interests lie in the intersection of agricultural science and data-driven technologies, with a particular focus on utilizing time series prediction for crop evapotranspiration and the development of intelligent irrigation systems. Accurate forecasting of crop evapotranspiration is crucial for optimizing irrigation practices, conserving water resources, and enhancing agricultural sustainability. I am particularly interested in leveraging advanced time series prediction models, such as deep learning algorithms, to improve the accuracy and reliability of short-term daily reference crop evapotranspiration forecasts. By integrating historical meteorological data and real-time weather forecasts, these models can provide valuable insights for real-time irrigation decision-making and regional water resource management. In addition, I am deeply engaged in the development and optimization of intelligent irrigation systems. These systems, powered by IoT technologies, soil moisture sensors, and AI algorithms, can significantly enhance water use efficiency and adaptability to changing environmental conditions.