Enhancing Vision Transformers with Kolmogorov–Arnold Networks for Plant Leaf Disease Classification

Abstract

Abstract Early and accurate detection of plant diseases is vital for global food security and sustainable agriculture. While deep learning offers promising solutions, there is a continuous need for architectures that enhance learning capacity and efficiency. This study introduces ViT-KAN, an innovative hybrid model merging the powerful feature extraction of Vision Transformers (ViT) with the flexible, learnable activation functions of Kolmogorov-Arnold Networks (KAN). By replacing the standard Multilayer Perceptron (MLP) classification head of ViT with a KAN module, the proposed architecture aims to better capture nonlinear patterns in agricultural images. Evaluated on the PlantVillage dataset for potato and maize leaf diseases using standard fivefold cross-validation, with final results reported as mean ± standard deviation across the five folds, the model was trained entirely from scratch. ViT-KAN achieved 99.49 ± 0.13% accuracy on the maize dataset and 98.28 ± 0.51% on the potato dataset, compared with 98.92 ± 0.40% and 97.77 ± 0.88%, respectively, for the standard ViT model. Beyond mean accuracy, ViT-KAN showed lower standard deviation across folds, while representative fold curves suggested smoother early training trajectories under the shared training configuration. These findings suggest that ViT-KAN is a promising alternative to conventional ViT-based classification models for plant disease diagnosis.

Plant phenotyping relevance

植物葉画像から病害状態を分類する新規ViT-KANモデルを開発・交差検証しており、病害表現型の取得・推定手法が研究の中心である。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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