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

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

扫码查看
原文链接
  • 万方数据
  • 维普
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.

banana leaf diseasesimage denoisingGhost ModuleResNeSt ModuleConvolutional Neural NetworksGR-ARNet

Jinsheng Deng、Weiqi Huang、Guoxiong Zhou、Yahui Hu、Liujun Li、Yanfeng Wang

展开 >

College of Electronic Information and Physics,Central South University of Forestry and Technology,Changsha 410004,China

Plant Protection Research Institute,Hunan Academy of Agricultural Sciences,Changsha 410125,China

Department of Soil and Water Systems,College of Agricultural & Life Sciences,University of Idaho,Moscow 83844,USA

College of Systems Engineering,National University of Defense Technology,Changsha 410073,China

展开 >

2024

10.1016/j.jia.2023.11.037

农业科学学报(英文)
中国农业科学院农业信息研究所

农业科学学报(英文)

CSTPCD
影响因子：0.576
ISSN：2095-3119
年,卷(期)：2024.23(10)