文献类型： 外文期刊
作者： Chen, Xiao 1 ; Zhou, Guoxiong 1 ; Chen, Aibin 1 ; Yi, Jizheng 1 ; Zhang, Wenzhuo 1 ; Hu, Yahui 2 ;
作者机构： 1.Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China
2.Hunan Acad Agr Sci, Plant Protect Inst, Changsha 410125, Hunan, Peoples R China
关键词： Tomato leaf disease recognition; Image enhancement; Residual network; B-ARNet
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）
ISSN： 0168-1699
年卷期： 2020 年 178 卷
页码：
收录情况： SCI
摘要： In the existing machine vision technology for tomato leaf disease recognition, due to interference from the external environment, it is easy to generate noise during image acquisition, and the characteristics of different diseases are similar, which makes image disease recognition difficult. Therefore, we propose a new framework for tomato leaf disease recognition. First, the image is denoised and enhanced by Binary Wavelet Transform combined with Retinex (BWTR), noise points and edge points are removed, and important texture information is retained. Then, the tomato leaves were separated from the background using KSW optimizatied by Artificial Bee Colony algorithm (ABCK). Finally, the Both-channel Residual Attention Network model (B-ARNet) was used to identify the pictures. The application results of 8616 images show that the overall detection accuracy is about 89%. Experiments show that the tomato leaf disease recognition method based on the combination of ABCKBWTR and B-ARNet is effective.
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