DS-MENet for the classification of citrus disease

文献类型： 外文期刊

第一作者： Liu, Xuyao

作者： Liu, Xuyao;Hu, Yaowen;Zhou, Guoxiong;Cai, Weiwei;He, Mingfang;Zhan, Jialei;Hu, Yahui;Li, Liujun

作者机构： Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha, Peoples R China;Hunan Acad Agr Sci, Plant Protect Res Inst, Changsha, Peoples R China;Univ Missouri Rolla, Dept Civil Architectural & Environm Engn, Rolla, MO USA

关键词： citrus disease detection; depthwise separable convolution; ReMish; multi-channel fusion backbone enhancement method; DS-MENet; image enhancement

期刊名称：FRONTIERS IN PLANT SCIENCE （ 2021影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life.

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