Evaluation of Data Augmentation to Image-Based Plant Disease Detection

Abstract

In this paper, we discuss about the evaluation of data augmentation to improve the accuracy for detecting plant diseases. Recently, researches on image-based plant disease detection using deep learning have been conducted. The researches require a huge number of training data, however, it is difficult to obtain so much data. Therefore, the authors focus an application of data augmentation to image-based plant disease detection. In many cases, it is known that data augmentation is effective, however, in some cases performance might be worse. As the condition that the performance of data augmentation deteriorates is not clear, the further researches are required. The authors propose to apply Frechet Inception Distance (FID) to the evaluation of data augmentation. In this study, we investigate the correlation between FID score and performance of data augmentation.