Y–Net: Identification of Typical Diseases of Corn Leaves Using a 3D–2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module

Abstract

Corn diseases are one of the significant constraints to high-quality corn production, and accurate identification of corn diseases is of great importance for precise disease control. Corn anthracnose and brown spot are typical diseases of corn, and the early symptoms of the two diseases are similar, which can be easily misidentified by the naked eye. In this paper, to address the above problems, a three-dimensional-two-dimensional (3D-2D) hybrid convolutional neural network (CNN) model combining a band selection module is proposed based on hyperspectral image data, which combines band selection, attention mechanism, spatial-spectral feature extraction, and classification into a unified optimization process. The model first inputs hyperspectral images to both the band selection module and the attention mechanism module and then sums the outputs of the two modules as inputs to a 3D-2D hybrid CNN, resulting in a Y-shaped architecture named Y-Net. The results show that the spectral bands selected by the band selection module of Y-Net achieve more reliable classification performance than traditional feature selection methods. Y-Net obtained the best classification accuracy compared to support vector machines, one-dimensional (1D) CNNs, and two-dimensional (2D) CNNs. After the network pruned the trained Y-Net, the model size was reduced to one-third of the original size, and the accuracy rate reached 98.34%. The study results can provide new ideas and references for disease identification of corn and other crops.

Plant phenotyping relevance

トウモロコシ葉の病害状態をハイパースペクトル画像から識別するCNN手法を開発・比較しており、植物病害フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe a self-collected hyperspectral corn leaf disease dataset (200 leaves, 6264 ROIs) and the Y-Net model, but contain no public data deposit, no author code/model availability statement, and no repository or URL for any paper-specific asset. Only the article itself and generic tool mentions (EN

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