**Rice Leaf Blast Classification Method Based on Fused Features and OneȬDimensional Deep Convolutional Neural Network**

**Shuai Feng 1, Yingli Cao 1,2, Tongyu Xu 1,2,\*, Fenghua Yu 1,2, Dongxue Zhao <sup>1</sup> and Guosheng Zhang <sup>1</sup>**


This paper developed seven one-dimensional deep convolutional neural network (DCNN) models to determine the best classification features and classification models for the five disease classes of leaf blast in order to improve the accuracy of grading the disease. It first pre-processed the hyperspectral imaging data to extract rice leaf samples of five disease classes, and the number of samples was increased by data-augmentation methods; then, spectral feature wavelengths, vegetation indices, and texture features were obtained based on the amplified sample data, which were used to construct CNN-based models. Finally, the proposed models were compared and analyzed with the Inception V3, ZF-Net, TextCNN, and bidirectional gated recurrent unit (BiGRU); support vector machine (SVM); and extreme learning machine (ELM) models in order to determine the best classification features and classification models for different disease classes of leaf blast. The experimental results also showed that the DCNN models provided better classification capability for disease classification than the Inception V3, ZF-Net, TextCNN, BiGRU, SVM, and ELM classification models. The SPA + TFs-DCNN achieved the best classification accuracy with an overall accuracy (OA) and Kappa of 98.58% and 98.22%, respectively. In terms of the classification of the specific different disease classes, the F1-scores for diseases of classes 0, 1, and 2 were all 100%, while the F1-scores for diseases of classes 4 and 5 were 96.48% and 96.68%, respectively. This study provides a new method for the identification and classification of rice leaf blast and a research basis for assessing the extent of the disease in the field.

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