Abstract
Global climate change and water resource shortages pose unprecedented challenges to agricultural production and water resource management. Traditional water resource management methods are struggling to address the complex environmental dynamics and varying water demands of different crops. The rapid development of artificial intelligence (AI) offers new opportunities to establish more efficient and intelligent agricultural water resource management systems. This paper systematically explores AI-based virtual experiments and simulation methods for agrarian water resources, proposing an overall design framework for a virtual farm laboratory. It focuses on analyzing core data processing technologies and the integrated application methods of crop growth models, hydrological models, and climate models. The study further investigates the specific applications of AI technologies, such as machine learning, deep learning, and reinforcement learning, in optimizing irrigation strategies, simulating water resource allocation under multiple climate scenarios, improving water use efficiency, and optimizing crop variety selection. Case studies validate the feasibility and practical effectiveness of virtual experiments, revealing the significant value of AI-based virtual experiments in addressing climate change, optimizing agricultural water efficiency, and enhancing agricultural production management. This paper aims to provide technical insights for research and practice in agricultural water resource management and highlights key future research directions and potential challenges.
