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In-Field Citrus Disease Classification via Convolutional Neural Network from Smartphone Images

文献类型： 外文期刊

作者： Yang, Changcai 1 ; Teng, Zixuan 1 ; Dong, Caixia 1 ; Lin, Yaohai 1 ; Chen, Riqing 1 ; Wang, Jian 2 ;

作者机构： 1.Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou 350002, Peoples R China

2.Ningxia Acad Agr & Forestry Sci, Yinchuan 750002, Ningxia, Peoples R China

关键词： citrus diseases; classification; convolutional neural network; transfer learning; smartphone image; field image

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）

ISSN：

年卷期： 2022 年 12 卷 9 期

页码：

收录情况： SCI

摘要： A high-efficiency, nondestructive, rapid, and automatic crop disease classification method is essential for the modernization of agriculture. To more accurately extract and fit citrus disease image features, we designed a new 13-layer convolutional neural network (CNN13) consisting of multiple convolutional layer stacks and dropout in this study. To address the problem created by the uneven number of disease images in each category, we used the VGG16 network module for transfer learning, which we combined with the proposed CNN13 to form a new joint network, which we called OplusVNet. To verify the performance of the proposed OplusVNet network, we collected 1869 citrus pest and disease images and 202 normal citrus images from the field. The experimental results showed that the proposed OplusVNet can more effectively solve the problem caused by uneven data volume and has higher recognition accuracy, especially for image categories with a relatively small data volume. Compared with the state of the art networks, the generalization ability of the proposed OplusVNet network is stronger for classifying diseases. The classification accuracy of the model prediction results was 0.99, indicating the model can be used as a reference for crop image classification.

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