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

文献类型： 外文期刊

第一作者： Deng, Jinsheng

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

作者机构：

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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