Synergistic Optimization of Agrivoltaic Systems: A Review of Intelligent Equipment Control for Agricultural Production Adaptability
Agrivoltaic systems configure photovoltaic power generation and agricultural production within the same land space, providing a new pathway for alleviating the conflict between energy development and farmland conservation; however, array shading and structural constraints also alter crop growth and agricultural equipment operating environments. Following the PRISMA process, this review searched studies published from 2010 to 2026 in the Web of Science Core Collection, Scopus, and China National Knowledge Infrastructure, and ultimately included 206 publications. The review focuses on array-induced environmental reconfiguration, equipment adaptation, light–thermal sensing and prediction, and coordinated agrivoltaic operation. Existing evidence indicates that the environmental effects of agrivoltaic systems are clearly influenced by climate and array configuration; in representative vertical or tracking systems, annual-scale photosynthetically active radiation decreased by approximately 11% to 34%. Mismatch among module height, row spacing, and implement width reduces field-operation efficiency, which fell to approximately 45% under severe mismatch in some experiments; elevated arrays also cause GNSS signal attenuation and increase the difficulty of continuous positioning beneath the panels. Because existing studies differ considerably in site conditions, evaluation indicators, and validation periods, the above results mainly reflect representative performance ranges. Matching criteria between photovoltaic arrays and agricultural machinery have not yet been established, long-term field-measured data in complex field environments are insufficient, model adaptability across regions is limited, and coordinated scheduling of agricultural production and photovoltaic operation and maintenance remains inadequate. Overall, coordinated design of arrays and agricultural machinery, multi-sensor fusion navigation, environmental prediction corrected for array structure, and hierarchical scheduling under safety constraints are the main development directions for intelligent coordinated operation of agrivoltaic systems.