Agrivoltaics is becoming an important pathway for coordinating renewable energy development, agricultural production, land-use efficiency, and rural low-carbon transition. This bookprovides a systematic research monograph on the integration of artificial intelligence with agrivoltaic systems. It covers the concepts, frameworks, enabling technologies, and engineering applications of AI-powered agrivoltaics, with particular attention to farming-PV and aquaculture-PV systems. The purpose of the book is to establish a coherent AI-based methodological framework for agrivoltaics and smart energy-food systems. This book aims to connect photovoltaic generation, agricultural production, system planning, and energy management through artificial intelligence and provide readers with an integrated framework spanning data and scenario generation, planning, control, and engineering implementation.

The book presents several results, methods, and perspectives of particular interest. These include generative AI methods for simulating agricultural meteorological scenarios, graph clustering methods for representative scenario extraction and capacity planning, deep reinforcement learning methods for coordinated control of photovoltaic power systems, and practical case studies based on Chinese farming-PV and aquaculture-PV projects. These methods are expected to help readers understand how AI can improve uncertainty modeling, planning reliability, operational efficiency, and engineering decision-making in agrivoltaic systems.