{"title":"Crop pest recognition based on a modified capsule network","authors":"Shanwen Zhang, Rongzhi Jing, Xiaoli Shi","doi":"10.1080/21642583.2022.2074168","DOIUrl":null,"url":null,"abstract":"Crop pest insects seriously affect yield and quality of crops, and pesticide control methods cause severe environmental pollution, which has inextricably influenced people’s daily lives. Crop pest identification in the field is crucial components of pest control. It is much more complex than generic object recognition due to the apparent differences in the same pest species in the field with various shapes, colours, sizes and complex background. A crop pest recognition method is proposed based on a modified capsule network (MCapsNet). In MCapsNet, a capsule network is used to improve the traditional convolutional neural network (CNN), and an attention module is introduced to capture the most important classification features and speed up the network training. The experimental results on a pest image dataset validate that the proposed method is effective and feasible in classifying various types of insects in field crops and can be implemented in the agriculture sector for crop protection.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2022.2074168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 3
Abstract
Crop pest insects seriously affect yield and quality of crops, and pesticide control methods cause severe environmental pollution, which has inextricably influenced people’s daily lives. Crop pest identification in the field is crucial components of pest control. It is much more complex than generic object recognition due to the apparent differences in the same pest species in the field with various shapes, colours, sizes and complex background. A crop pest recognition method is proposed based on a modified capsule network (MCapsNet). In MCapsNet, a capsule network is used to improve the traditional convolutional neural network (CNN), and an attention module is introduced to capture the most important classification features and speed up the network training. The experimental results on a pest image dataset validate that the proposed method is effective and feasible in classifying various types of insects in field crops and can be implemented in the agriculture sector for crop protection.
期刊介绍:
Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory