Apple Leaf Disease Identification with a Small and Imbalanced Dataset Based on Lightweight Convolutional Networks.

Abstract

The intelligent identification and classification of plant diseases is an important research objective in agriculture. In this study, in order to realize the rapid and accurate identification of apple leaf disease, a new lightweight convolutional neural network RegNet was proposed. A series of comparative experiments had been conducted based on 2141 images of 5 apple leaf diseases (rust, scab, ring rot, panonychus ulmi, and healthy leaves) in the field environment. To assess the effectiveness of the RegNet model, a series of comparison experiments were conducted with state-of-the-art convolutional neural networks (CNN) such as ShuffleNet, EfficientNet-B0, MobileNetV3, and Vision Transformer. The results show that RegNet-Adam with a learning rate of 0.0001 obtained an average accuracy of 99.8% on the validation set and an overall accuracy of 99.23% on the test set, outperforming all other pre-trained models. In other words, the proposed method based on transfer learning established in this research can realize the rapid and accurate identification of apple leaf disease.

Plant phenotyping relevance

リンゴ葉の病害状態を画像から分類する軽量CNNを提案し、複数モデルとの比較検証を行っており、植物表現型（病害状態）の取得・推定が中心である。

Code and data availability

The supplied blocks describe a self-collected apple leaf disease image dataset (2141 images) and RegNet-based classification experiments, but contain no public deposit, availability statement, or URL for the dataset, images, code, or trained models. The only URL present is the CC BY license link, which is not a paper-­

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