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

IEEE Access

Guoxiong Zhou¹  Mingfang He¹  Mingjie Lv¹  Aibin Chen¹  Wenzhuo Zhang¹  Yahui Hu² 

[1] College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China;Institute of Plant Protection, Hunan Academy of Agricultural Sciences, Changsha, China;

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

DOI  :  10.1109/ACCESS.2020.2982443

来源: DOAJ

【 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.

【 授权许可】

Unknown