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.

使用基于卷积神经网络与对抗性生成网络的最新技术，在农业光伏组合中成功检测出树影对植物生长、微气候和蒸散发的影响，但即使面临运动物体和实时监测等挑战，仍需要进一步针对性地开发更复杂的神经网络检测算法与控制系统整合，以提高生产力、增加农民的收益，并支持环境与土地保护。

基于AI的农业光伏场中阴影模型检测