Xueqian Fu , Qiaoyu Ma , Feifei Yang , Chunyu Zhang , Xiaolong Zhao , Fuhao Chang , Lingling Han
{"title":"Crop pest image recognition based on the improved ViT method","authors":"Xueqian Fu , Qiaoyu Ma , Feifei Yang , Chunyu Zhang , Xiaolong Zhao , Fuhao Chang , Lingling Han","doi":"10.1016/j.inpa.2023.02.007","DOIUrl":null,"url":null,"abstract":"<div><p>The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality, which threaten macroeconomic stability and sustainable development. However, the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency. The recognition method based on pattern recognition and deep learning can automatically fit image features, and use features to classify and predict images. This study introduced the improved Vision Transformer (ViT) method for crop pest image recognition. Among them, the region with the most obvious features can be effectively selected by block partition. The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area. In the experiment, data with 7 classes of examples are used for verification. It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology, accurately judge the crop diseases and pests category, provide method reference for agricultural diseases and pests identification research, and further optimize the crop diseases and pests control work for agricultural workers in need.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317323000173/pdfft?md5=6fed672082a3cb1962c98992473957f9&pid=1-s2.0-S2214317323000173-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317323000173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality, which threaten macroeconomic stability and sustainable development. However, the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency. The recognition method based on pattern recognition and deep learning can automatically fit image features, and use features to classify and predict images. This study introduced the improved Vision Transformer (ViT) method for crop pest image recognition. Among them, the region with the most obvious features can be effectively selected by block partition. The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area. In the experiment, data with 7 classes of examples are used for verification. It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology, accurately judge the crop diseases and pests category, provide method reference for agricultural diseases and pests identification research, and further optimize the crop diseases and pests control work for agricultural workers in need.
期刊介绍：
Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
