A Method for Sugarcane Disease Identification Based on Improved ShuffleNetV2 Model

Abstract

Abstract Rapid and accurate identification of sugarcane diseases is an important way to improve sugarcane yield. Therefore, this study proposes an improved model based on ShuffleNetV2 network (Im-ShuffleNetV2) for sugarcane disease identification. Firstly, we incorporated the ECA (Enhanced Channel Attention) attention mechanism into ShuffleNetV2, enhancing the network's ability to extract features and detect sugarcane lesion areas. Secondly, a new multi-scale feature extraction branch and Transformer module have been introduced, further improving the independent learning ability of the network. Finally, a large number of numerical results have demonstrated the advantages of the proposed model in terms of parameter size and sugarcane disease identification accuracy. Just as Im-ShuffleNetV2 only has a parameter of 0.4MB, it has significant advantages over parameters such as EfficientV2-S (55.6MB), MobileNetV2 (8.73MB), MobileViT XX small (3.76MB), FasterNetT2 (52.4MB), AlexNet (55.6MB), and MobileNetV3 Large (16.2MB). In addition, compared with the ShuffleNetV2 network, the accuracy has improved by 3.4%. This model not only improves the accuracy of sugarcane leaf disease detection, but also demonstrates the advantage of lightweight, providing valuable reference for future research in the field of sugarcane.

Plant phenotyping relevance

サトウキビ葉の病斑領域・病害を画像から識別する軽量深層学習モデルを開発し、既存モデルと精度・パラメータ数を比較検証しているため、植物病害表現型の取得手法が中心である。

Code and data availability

The paper uses a Kaggle sugarcane disease image dataset and an improved ShuffleNetV2 model, but provides no public code, model checkpoints, or dataset URL. The declarations state 'Availability of data and materials: not applicable', and no authors' repository or deposit is mentioned anywhere in the supplied blocks. The

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