PlantPlotGAN: A Physics-Informed Generative Adversarial Network for
  Plant Disease Prediction
Felipe A. Lopes Vasit Sagan Flavio Esposito
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.