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Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet

文献类型： 外文期刊

作者： Lv, Mingjie 1 ; Zhou, Guoxiong 1 ; He, Mingfang 1 ; Chen, Aibin 1 ; Zhang, Wenzhuo 1 ; Hu, Yahui 2 ;

作者机构： 1.Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Peoples R China

2.Hunan Acad Agr Sci, Inst Plant Protect, Changsha 410125, Peoples R China

关键词： Image enhancement; dilated convolution; multi-scale convolution; maize leaf disease; convolutional neural network

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2020 年 8 卷

页码：

收录情况： SCI

摘要： The identification of maize leaf diseases will meet great challenges because of the difficulties in extracting lesion features from the constant-changing environment, uneven illumination reflection of the incident light source and many other factors. In this paper, a novel maize leaf disease recognition method is proposed. In this method, we first designed a maize leaf feature enhancement framework with the capability of enhancing the features of maize under the complex environment. Then a novel neural network is designed based on backbone Alexnet architecture, named DMS-Robust Alexnet. In the DMS-Robust Alexnet, dilated convolution and multi-scale convolution are combined to improve the capability of feature extraction. Batch normalization is performed to prevent network over-fitting while enhancing the robustness of the model. PRelu activation function and Adabound optimizer are employed to improve both convergence and accuracy. In experiments, it is validated from different perspectives that the maize leaf disease feature enhancement algorithm is conducive to improving the capability of the DMS-Robust Alexnet identification. Our method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases.

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