基于神经网络的橄榄树无人机检测苛养木杆菌病

Irene Mazzilli, Gianmario Mirabile, P. Lino, G. Maione, A. Rybakov, N. Svishchev, Ileana Blanco, L. De Bellis, A. Luvisi
{"title":"基于神经网络的橄榄树无人机检测苛养木杆菌病","authors":"Irene Mazzilli, Gianmario Mirabile, P. Lino, G. Maione, A. Rybakov, N. Svishchev, Ileana Blanco, L. De Bellis, A. Luvisi","doi":"10.1109/CNNA49188.2021.9610752","DOIUrl":null,"url":null,"abstract":"This paper presents a fully automated procedure for the detection of trees affected by Xylella Fastidiosa using UAVs and convolutional neural networks. Drones are able to collect an adequate number of olive leaf images to detect the presence of disease symptoms. Several neural networks are trained to compare results and determine the best solution.","PeriodicalId":325231,"journal":{"name":"2021 17th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"UAV Inspection of Olive Trees for the Detection of Xylella Fastidiosa Disease Using Neural Networks\",\"authors\":\"Irene Mazzilli, Gianmario Mirabile, P. Lino, G. Maione, A. Rybakov, N. Svishchev, Ileana Blanco, L. De Bellis, A. Luvisi\",\"doi\":\"10.1109/CNNA49188.2021.9610752\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a fully automated procedure for the detection of trees affected by Xylella Fastidiosa using UAVs and convolutional neural networks. Drones are able to collect an adequate number of olive leaf images to detect the presence of disease symptoms. Several neural networks are trained to compare results and determine the best solution.\",\"PeriodicalId\":325231,\"journal\":{\"name\":\"2021 17th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 17th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA49188.2021.9610752\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 17th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA49188.2021.9610752","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

本文提出了一种利用无人机和卷积神经网络对受苛养木杆菌影响的树木进行全自动检测的方法。无人机能够收集足够数量的橄榄叶图像来检测疾病症状的存在。几个神经网络被训练来比较结果并确定最佳解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV Inspection of Olive Trees for the Detection of Xylella Fastidiosa Disease Using Neural Networks
This paper presents a fully automated procedure for the detection of trees affected by Xylella Fastidiosa using UAVs and convolutional neural networks. Drones are able to collect an adequate number of olive leaf images to detect the presence of disease symptoms. Several neural networks are trained to compare results and determine the best solution.
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:604180095
Book学术官方微信