Investigating Performance MLP-Mixer and gMLP for Crop Leaf Diseases

Abstract

Early recognition of plant diseases is crucial, and one practical approach is using deep learning models. Models, such as MLP-Mixer and gMLP, based on multi-layer perceptron, offer a compelling option due to their simple architecture. This study aimed to assess the performance of these models in classifying crop leaf diseases. Our findings reveal that both MLP-Mixer and gMLP models, with their similar architectures, exhibit promising performance. We conducted tests using public potato and wheat datasets to evaluate their classification performance. Furthermore, we incorporated gradient centralization during training to enhance the models’ generalization performance. The results indicate that both MLP-Mixer and gMLP achieved classification performance above 0.9100 for both datasets. Specifically, MLP-Mixer achieved 0.9819, and gMLP achieved 0.9873 for potato leaf diseases, while for wheat leaf diseases, MLP-Mixer achieved 0.9121, and gMLP achieved 0.9189. These outcomes emphasize the potential of these models in classifying and identifying diseases in crop leaves.

Plant phenotyping relevance

作物葉の病害状態を画像から分類する深層学習手法を比較・評価しており、植物表現型取得が研究の中心である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.