Plant Disease Identification Based on Encoder–Decoder Model

Abstract

Plant disease identification is a crucial issue in agriculture, and with the advancement of deep learning techniques, early and accurate identification of plant diseases has become increasingly critical. In recent years, the rise of vision transformers has attracted significant attention from researchers in various vision-based application areas. We designed a model with an encoder–decoder architecture to efficiently classify plant diseases using a transfer learning approach, which effectively recognizes a large number of plant diseases in multiple crops. The model was tested on the “PlantVillage”, “FGVC8”, and “EMBRAPA” datasets, which contain leaf information from crops such as apples, soybeans, tomatoes, and potatoes. These datasets cover diseases caused by fungi, including rust, spot, and scab, as well as viral diseases such as leaf curl. The model’s performance was rigorously evaluated on datasets, and the results demonstrated its high accuracy. The model achieved 99.9% accuracy on the “PlantVillage” dataset, 97.4% on the “EMBRAPA” dataset, and 91.5% on the “FGVC8” dataset, showcasing its competitiveness with other state-of-the-art models. This study provides a robust and reliable solution for plant disease classification and contributes to the advancement of precision agriculture.

Plant phenotyping relevance

葉画像から植物病害を分類するエンコーダ・デコーダ法を開発し、複数データセットで性能評価しており、植物状態の取得・推定手法が研究の中心である。

Code and data availability

The paper uses three public leaf-disease image datasets (PlantVillage, FGVC8, EMBRAPA), but these are pre-existing third-party benchmark datasets rather than paper-specific assets produced by the authors. No author analysis code, trained model checkpoints, or supplementary data repository is disclosed; the Data/Code Av

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