Monitoring plantations is crucial for crop management and producing healthy harvests. Unmanned Aerial Vehicles (UAVs) have been used to collect multispectral images that aid in this monitoring. However, given the number of hectares to be monitored and the limitations of flight, plant disease signals become visually clear only in the later stages of plant growth and only if the disease has spread throughout a significant portion of the plantation. This limited amount of relevant data hampers the prediction models, as the algorithms struggle to generalize patterns with unbalanced or unrealistic augmented datasets effectively. To address this issue, we propose PlantPlotGAN, a physics-informed generative model capable of creating synthetic multispectral plot images with realistic vegetation indices. These indices served as a proxy for disease detection and were used to evaluate if our model could help increase the accuracy of prediction models. The results demonstrate that the synthetic imagery generated from PlantPlotGAN outperforms state-of-the-art methods regarding the Fr\'echet inception distance. Moreover, prediction models achieve higher accuracy metrics when trained with synthetic and original imagery for earlier plant disease detection compared to the training processes based solely on real imagery.

翻译：监测种植园对作物管理和健康收成至关重要。无人机已被用于采集多光谱图像以辅助这一监测过程。然而，考虑到需要监测的公顷面积以及飞行限制，植物病害信号只有在植物生长后期且病害已扩散至种植园大部分区域时才变得视觉清晰。这种相关数据的有限性阻碍了预测模型的发展，因为算法难以有效处理不平衡或非真实增强数据集中的模式泛化问题。为解决这一问题，我们提出PlantPlotGAN——一种物理信息生成模型，能够生成具有真实植被指数的合成多光谱地块图像。这些指数作为病害检测的替代指标，并用于评估模型是否有助于提高预测模型的准确性。结果表明，PlantPlotGAN生成的合成图像在弗雷歇初始距离上优于现有方法。此外，与仅基于真实图像的训练过程相比，使用合成图像与原始图像联合训练时，预测模型在早期植物病害检测中能获得更高的准确率指标。