PPIG: Productive and Pathogenic Image Generation for Plant Disease Diagnosis

Abstract

Image-based autonomous diagnosis for plants is a difficult task since plant symptoms are visually subtle. This subtlety leads to the system overfitting as it sometimes responds to non-essential parts in images such as background or sunlight conditions. Thus, this causes a significant drop in performance when diagnosing diseases in different test fields. Several data augmentation methods utilizing generative adversarial networks (GANs) have been proposed to address this overfitting problem. However, performance improvement is limited due to the limited variety of generated images. This study proposes a productive and pathogenic image generation (PPIG) technique, a framework for generating varied and quality plant images to train the diagnostic systems. PPIG is comprised of two phases: the bulk production phase and the pathogenic phase. In the first phase, a number of healthy leaf images are generated to form the basis for the generation of disease images. Then, in the second phase, the symptomatic characteristics are added to the leaf part of the generated healthy images. In this study, we conducted experiments to evaluate PPIG using test images taken in different fields from the training images, assuming six disease classes of cucumber leaves. The proposed PPIG can generate natural-looking, healthy and disease images, and data augmentation using these images effectively improved the robustness of the diagnostic system. Experiments on 8,834 test images taken in different fields from 53,045 training images show that our proposal improved the disease diagnostic performance from the baseline by 9.4% for the macro-average F1-score. Moreover, it also outperformed the previous cutting-edge data augmentation methodology by 4.5%.