Abstract
With the increasing severity of global climate change and water resource scarcity, the agricultural sector faces a growing demand for efficient water utilization and sustainable management. Intelligent irrigation systems based on artificial intelligence (AI) technologies have emerged as a key solution to address these challenges, thanks to their high efficiency in resource optimization. This paper systematically reviews the core technologies of intelligent irrigation systems, including sensor networks, soil moisture monitoring, nitrogen cycle modeling, and automated control systems, with a particular focus on the application of AI techniques in irrigation strategy optimization. Innovative contributions of machine learning, deep learning, and reinforcement learning algorithms to precise water resource management are highlighted. By analyzing the application of AI in real-time data processing, predictive modeling, resource allocation optimization, and sustainable development management, this paper presents the latest research progress and typical practical cases in the field. Major challenges such as data quality control, model accuracy improvement, and system compatibility issues are also discussed in depth, along with potential technical pathways to address these challenges. Ultimately, the paper envisions the future potential of AI-driven intelligent irrigation systems in enhancing water use efficiency, optimizing nitrogen uptake, and promoting agricultural sustainability, thereby providing a valuable reference for future research and practice.
