Denoising Diffusion Probabilistic Models and Transfer Learning for citrus disease diagnosis.

Abstract

Problems Plant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations in the distribution of state samples, and the lack of sufficient real disease databases weakens the information carried by the samples, posing challenges for accurately training models. Aim This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in diagnosing citrus diseases with a small sample. Methods Two training methods are proposed: The Method 1 employs the DDPM to generate synthetic images for data augmentation. The Swin Transformer model is then used for pre-training on the synthetic dataset produced by DDPM, followed by fine-tuning on the original citrus leaf images for disease classification through transfer learning. The Method 2 utilizes the pre-trained Swin Transformer model on the ImageNet dataset and fine-tunes it on the augmented dataset composed of the original and DDPM synthetic images. Results and conclusion The test results indicate that Method 1 achieved a validation accuracy of 96.3%, while Method 2 achieved a validation accuracy of 99.8%. Both methods effectively addressed the issue of model overfitting when dealing with a small dataset. Additionally, when compared with VGG16, EfficientNet, ShuffleNet, MobileNetV2, and DenseNet121 in citrus disease classification, the experimental results demonstrate the superiority of the proposed methods over existing approaches to a certain extent.

Plant phenotyping relevance

柑橘葉画像から病害状態を推定する画像解析手法を開発・比較し、データ拡張と分類性能を検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper describes a self-collected citrus leaf dataset (2,648 field images) and DDPM/Swin Transformer experiments, but no supplied block contains any public deposit, availability statement, or URL for the dataset, synthetic images, code, or trained models. No qualifying paper-specific public asset exists.

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