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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.

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