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

作　　者：Jinsheng Deng Weiqi Huang Guoxiong Zhou Yahui Hu Liujun Li Yanfeng Wang 

机构地区：[1]College of Electronic Information and Physics,Central South University of Forestry and Technology,Changsha 410004,China [2]Plant Protection Research Institute,Hunan Academy of Agricultural Sciences,Changsha 410125,China [3]Department of Soil and Water Systems,College of Agricultural&Life Sciences,University of Idaho,Moscow 83844,USA [4]College of Systems Engineering,National University of Defense Technology,Changsha 410073,China

出　　处：《Journal of Integrative Agriculture》2024年第10期3554-3575,共22页农业科学学报（英文版）

基　　金：supported by the Changsha Municipal Natural Science Foundation,China(kq2014160);in part by the Key Projects of Department of Education of Hunan Province,China(21A0179);the Hunan Key Laboratory of Intelligent Logistics Technology,China(2019TP1015);the National Natural Science Foundation of China(61902436)。

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.

关 键 词：banana leaf diseases image denoising Ghost Module Res Ne St Module Convolutional Neural Networks GR-ARNet 

分 类 号：S436.68[农业科学—农业昆虫与害虫防治]