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Agro-photovoltaic (APV) is a growing farming practice that combinesagriculture and solar photovoltaic projects within the same area. This emergingmarket is expected to experience significant growth in the next few years, witha projected investment of $9 billion in 2030. Identifying shadows is crucial tounderstanding the APV environment, as they impact plant growth, microclimate,and evapotranspiration. In this study, we use state-of-the-art CNN andGAN-based neural networks to detect shadows in agro-PV farms, demonstratingtheir effectiveness. However, challenges remain, including partial shadowingfrom moving objects and real-time monitoring. Future research should focus ondeveloping more sophisticated neural network-based shadow detection algorithmsand integrating them with control systems for APV farms. Overall, shadowdetection is crucial to increase productivity and profitability whilesupporting the environment, soil, and farmers.

AI driven shadow model detection in agropv farms