AI driven shadow model detection in agropv farms

Résumé : Agro-photovoltaic (APV) is a growing farming practice that combines agriculture and solar photovoltaic projects within the same area. This emerging market is expected to experience significant growth in the next few years, with a projected investment of $9 billion in 2030. Identifying shadows is crucial to understanding the APV environment, as they impact plant growth, microclimate, and evapotranspiration. In this study, we use state-of-the-art CNN and GAN-based neural networks to detect shadows in agro-PV farms, demonstrating their effectiveness. However, challenges remain, including partial shadowing from moving objects and real-time monitoring. Future research should focus on developing more sophisticated neural network-based shadow detection algorithms and integrating them with control systems for APV farms. Overall, shadow detection is crucial to increase productivity and profitability while supporting the environment, soil, and farmers.