Data Augmentation Method for Plant Leaf Disease Recognition

• Published in: Applied sciences Vol. 13; no. 3; p. 1465
• Main Authors: Min, Byeongjun, Kim, Taehyun, Shin, Dongil, Shin, Dongkyoo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Accuracy; Bias; Classification; Climate change; convolutional attention; Crop damage; Crop diseases; Crop production; Data augmentation; Datasets; Deep learning; Diagnosis; Experiments; imbalanced dataset; Leaves; Manpower; Medical imaging; Neural networks; Performance enhancement; plant disease recognition; Plant diseases; Productivity; Translation
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13031465

Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a shortage of expert manpower is also deepening. Fortunately, research on various early diagnosis systems based on deep learning is actively underway to solve these problems, but the problem of lack of diversity in some hard-to-collect disease samples remains. This imbalanced data increases the bias of machine learning models, causing overfitting problems. In this paper, we propose a data augmentation method based on an image-to-image translation model to solve the bias problem by supplementing these insufficient diseased leaf images. The proposed augmentation method performs translation between healthy and diseased leaf images and utilizes attention mechanisms to create images that reflect more evident disease textures. Through these improvements, we generated a more plausible diseased leaf image compared to existing methods and conducted an experiment to verify whether this data augmentation method could further improve the performance of a classification model for early diagnosis of plants. In the experiment, the PlantVillage dataset was used, and the extended dataset was built using the generated images and original images, and the performance of the classification models was evaluated through the test set.