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Identification of banana leaf disease based on KVA and GR-ARNet

文献类型： 外文期刊

作者： Deng, Jinsheng 1 ; Huang, Weiqi 1 ; Zhou, Guoxiong 1 ; Hu, Yahui 2 ; Li, Liujun 3 ; Wang, Yanfeng 4 ;

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

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

3.Univ Idaho, Coll Agr & Life Sci, Dept Soil & Water Syst, Moscow, ID 83844 USA

4.Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Peoples R China

关键词： banana leaf diseases; image denoising; Ghost Module; ResNeSt Module; Convolutional Neural Networks; GR-ARNet

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：4.4； 五年影响因子：4.8 ）

ISSN： 2095-3119

年卷期： 2024 年 23 卷 10 期

页码：

收录情况： SCI

摘要： Banana is a significant crop, and three banana leaf diseases, including Sigatoka, Cordana and Pestalotiopsis, have the potential to have a serious impact on banana production. Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases. Therefore, this paper proposes a novel method to identify banana leaf diseases. First, a new algorithm called K-scale VisuShrink algorithm (KVA) is proposed to denoise banana leaf images. The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds, the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image. Then, this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net (GR-ARNet) based on Resnet50. In this, the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed; the ResNeSt Module adjusts the weight of each channel, increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification; the model's computational speed is increased using the hybrid activation function of RReLU and Swish. Our model achieves an average accuracy of 96.98% and a precision of 89.31% applied to 13,021 images, demonstrating that the proposed method can effectively identify banana leaf diseases.

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