Hyperspectral Plant Disease Forecasting Using Generative Adversarial Networks

Abstract

With a limited amount of arable land, increasing demand for food induced by growth in population can only be meet with more effective crop production and more resistant plants. Since crop plants are exposed to many different stress factors, it is relevant to investigate those factors as well as their behavior and reactions. One of the most severe stress factors are diseases, resulting in a high loss of cultivated plants. Our main objective is the forecasting of the spread of disease symptons on barley plants using a Cycle-Consistent Generative Adversarial Network. Our contributions are: (1) we provide a daily forecast for one week to advance research for better planning of plant protection measures, and (2) in contrast to most approaches which use only RGB images, we learn a model with hyperspectral images, providing an information-rich result. In our experiments, we analyze healthy barley leaves and leaves which were inoculated by powdery mildew. Images of the leaves were acquired daily with a hyperspectral microscope, from day 3 to day 14 after inoculation. We provide two methods for evaluating the predicted time series with respect to the reference time series.