Lightweight Multi-scale Convolutional Neural Network for Rice Leaf Disease Recognition  

作　　者：Chang Zhang Ruiwen Ni Ye Mu Yu Sun Thobela Louis Tyasi 

机构地区：[1]College of Information Technology,Jilin Agricultural University,Changchun,130118,China [2]Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center,Changchun,130118,China [3]Jilin Province Intelligent Environmental Engineering Research Center,Changchun,130118,China [4]Jilin Province Information Technology and Intelligent Agriculture Engineering Research Center,Changchun,130118,China [5]Department of Agricultural Economics and Animal Production,University of Limpopo,Private Bag X 1106,Sovenga,0727,Polokwane,South Africa

出　　处：《Computers, Materials & Continua》2023年第1期983-994,共12页计算机、材料和连续体（英文）

基　　金：supported by National key research and development program sub-topics[2018YFF0213606-03(Mu Y.,Hu T.L.,Gong H.,Li S.J.and Sun Y.H.)http://www.most.gov.cn];Jilin Province Science and Technology Development Plan focuses on research and development projects[20200402006NC(Mu Y.,Hu T.L.,Gong H.and Li S.J.)http://kjt.jl.gov.cn];Science and technology support project for key industries in southern Xinjiang[2018DB001(Gong H.,and Li S.J.)http://kjj.xjbt.gov.cn];Key technology R&D project of Changchun Science and Technology Bureau of Jilin Province[21ZGN29(Mu Y.,Bao H.P.,Wang X.B.)http://kjj.changchun.gov.cn].

摘　　要：In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of pattern recognition.The research and development of high-efficiency,highquality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective.This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network(CNN)model and proposes a recognitionmodel that combines amulti-scale convolution module with a neural network model based on Visual Geometry Group(VGG).The accuracy and loss of the training set and the test set are used to evaluate the performance of the model.The test accuracy of this model is 97.1%that has increased 5.87%over VGG.Furthermore,the memory requirement is 26.1M,only 1.6%of the VGG.Experiment results show that this model performs better in terms of accuracy,recognition speed and memory size.

关 键 词：Rice leaf diseases deep learning lightweight convolution neural networks VGG 

分 类 号：S511[农业科学—作物学] TP183[自动化与计算机技术—控制理论与控制工程]