气体传感器阵列灭蟑器对蜜蜂侵害的分类

A. Szczurek, M. Maciejewska, B. Bak, Jakub Wilk, J. Wilde, M. Siuda
{"title":"气体传感器阵列灭蟑器对蜜蜂侵害的分类","authors":"A. Szczurek, M. Maciejewska, B. Bak, Jakub Wilk, J. Wilde, M. Siuda","doi":"10.5220/0009171100610068","DOIUrl":null,"url":null,"abstract":"Infestation of bee colony with Varroa destructor proceeds exponentially. It is important to detect the disease at its very early stage. However, the distinction of later infestation stages is also practical. We proposed to apply gas sensor array measurements of beehive air as the source of information which may be useful for this kind of assessment. Honeybee infestation was classified into three categories: ‘low’, ‘medium’ and ‘high’, two categories: ‘low’ and ‘medium to high’, and another two categories: ‘high’ and ‘medium to low’. Responses of gas sensor array to beehive air were used as the input data of the classifier, which was trained to distinguish the categories. The results of the analysis demonstrated that category ‘low’ was determined most effectively, with an error rate of about 10%. Category ‘high’ was most difficult to determine. In this case the lowest error rate was about 20%. Based on our analysis, the approach based on binary classification was favoured and SVM outperformed ensemble of classification trees. It was found, that first several minutes of gas sensors exposure to beehive air were sufficient to attain effective classification. The presented method of varroosis determination, based on beehive air sensing with gas sensors is innovative and has high potential of application in beekeeping.","PeriodicalId":72028,"journal":{"name":"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks","volume":"8 1","pages":"61-68"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification of Honeybee Infestation by Varroa Destructor using Gas Sensor Array\",\"authors\":\"A. Szczurek, M. Maciejewska, B. Bak, Jakub Wilk, J. Wilde, M. Siuda\",\"doi\":\"10.5220/0009171100610068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Infestation of bee colony with Varroa destructor proceeds exponentially. It is important to detect the disease at its very early stage. However, the distinction of later infestation stages is also practical. We proposed to apply gas sensor array measurements of beehive air as the source of information which may be useful for this kind of assessment. Honeybee infestation was classified into three categories: ‘low’, ‘medium’ and ‘high’, two categories: ‘low’ and ‘medium to high’, and another two categories: ‘high’ and ‘medium to low’. Responses of gas sensor array to beehive air were used as the input data of the classifier, which was trained to distinguish the categories. The results of the analysis demonstrated that category ‘low’ was determined most effectively, with an error rate of about 10%. Category ‘high’ was most difficult to determine. In this case the lowest error rate was about 20%. Based on our analysis, the approach based on binary classification was favoured and SVM outperformed ensemble of classification trees. It was found, that first several minutes of gas sensors exposure to beehive air were sufficient to attain effective classification. The presented method of varroosis determination, based on beehive air sensing with gas sensors is innovative and has high potential of application in beekeeping.\",\"PeriodicalId\":72028,\"journal\":{\"name\":\"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks\",\"volume\":\"8 1\",\"pages\":\"61-68\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0009171100610068\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0009171100610068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

破坏瓦螨对蜂群的侵扰呈指数增长。在早期发现这种疾病是很重要的。然而,后期侵染阶段的区分也是实用的。我们建议将蜂窝空气的气体传感器阵列测量作为信息来源,这可能对这类评估有用。蜜蜂侵扰分为“低”、“中”和“高”三类,“低”和“中到高”两类,另外两类:“高”和“中到低”。将气体传感器阵列对蜂窝空气的响应作为分类器的输入数据,对分类器进行分类训练。分析结果表明,“低”类别的确定最有效,错误率约为10%。“高”类别最难确定。在这种情况下,最低错误率约为20%。基于我们的分析,基于二值分类的方法更受青睐,支持向量机优于分类树集合。研究发现,气体传感器暴露于蜂巢空气的最初几分钟足以获得有效的分类。本文提出的基于蜂箱空气传感和气体传感器的静脉曲张检测方法具有创新性,在养蜂业中具有很大的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Honeybee Infestation by Varroa Destructor using Gas Sensor Array
Infestation of bee colony with Varroa destructor proceeds exponentially. It is important to detect the disease at its very early stage. However, the distinction of later infestation stages is also practical. We proposed to apply gas sensor array measurements of beehive air as the source of information which may be useful for this kind of assessment. Honeybee infestation was classified into three categories: ‘low’, ‘medium’ and ‘high’, two categories: ‘low’ and ‘medium to high’, and another two categories: ‘high’ and ‘medium to low’. Responses of gas sensor array to beehive air were used as the input data of the classifier, which was trained to distinguish the categories. The results of the analysis demonstrated that category ‘low’ was determined most effectively, with an error rate of about 10%. Category ‘high’ was most difficult to determine. In this case the lowest error rate was about 20%. Based on our analysis, the approach based on binary classification was favoured and SVM outperformed ensemble of classification trees. It was found, that first several minutes of gas sensors exposure to beehive air were sufficient to attain effective classification. The presented method of varroosis determination, based on beehive air sensing with gas sensors is innovative and has high potential of application in beekeeping.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信