Application of a Latent Diffusion Model to Plant Disease Detection by Generating Unseen Class Images

Abstract

Deep learning-based methods have proven to be effective for various purposes in the agricultural sector. However, these methods require large amounts of labelled data, which are difficult to prepare and preprocess. To overcome this problem, we propose the use of a latent diffusion model for plant disease detection by generating unseen class images. In this study, we used images of healthy and diseased grape leaves as training datasets and utilized the latent diffusion model, known for its superior performance in image generation, to generate images of diseased apple leaves that were not included in this dataset. Image-to-image generation was utilized to preserve the original healthy leaf features, which enabled the appropriate image generation of diseased apple leaves. To ascertain whether the generated diseased apple leaf images could be used to detect leaf diseases, a deep learning-based classification model was trained to discriminate between diseased and healthy apple leaves from a dataset with a mixture of actual and generated images. Results showed that leaves were accurately classified, indicating that diseased apple leaves not included in the training data could be used to identify the actual diseased apple leaves. Our approach opens up new avenues for improving plant disease detection methods.

Plant phenotyping relevance

植物病害画像を生成して未見クラスの葉病害を検出する手法の開発が中心であり、葉の病害状態を画像から推定する植物フェノタイピング研究に該当する。

Code and data availability

The paper's plant-disease phenotyping analysis is built on the public PlantVillage leaf image dataset, which the authors explicitly used as training/input data. No authors' analysis code, generated image sets, or trained model checkpoints are stated to be publicly available.