基于生成对抗网络的作物病害识别系统文献综述

Aruna Mittal, Hridesh Gupta
{"title":"基于生成对抗网络的作物病害识别系统文献综述","authors":"Aruna Mittal, Hridesh Gupta","doi":"10.1109/ICTAI53825.2021.9673159","DOIUrl":null,"url":null,"abstract":"\"However, a deep learning network requires a large amount of data, and because certain plant lesion data is difficult to acquire and has a similar structure, deep learning has lately showed potential in the identification of plant lesions.\", The data set has to be increased by generating full plant lesion leaf pictures. To address this issue, this article offers a survey on technique for generating full and rare picture of plant lesion leaf that may be enhance the accuracy of classification network. Some of the benefits of our research in this article are a systematic survey on GAN based plant disease identification where many authors gave the theory and practical implementation on that. My approach has been shown to successfully extend plant lesion research and improve the classification network’s identification accuracy in the future.","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Systematic Literature Survey on Generative Adversarial Network Based Crop Disease Identification\",\"authors\":\"Aruna Mittal, Hridesh Gupta\",\"doi\":\"10.1109/ICTAI53825.2021.9673159\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\\"However, a deep learning network requires a large amount of data, and because certain plant lesion data is difficult to acquire and has a similar structure, deep learning has lately showed potential in the identification of plant lesions.\\\", The data set has to be increased by generating full plant lesion leaf pictures. To address this issue, this article offers a survey on technique for generating full and rare picture of plant lesion leaf that may be enhance the accuracy of classification network. Some of the benefits of our research in this article are a systematic survey on GAN based plant disease identification where many authors gave the theory and practical implementation on that. My approach has been shown to successfully extend plant lesion research and improve the classification network’s identification accuracy in the future.\",\"PeriodicalId\":278263,\"journal\":{\"name\":\"2021 International Conference on Technological Advancements and Innovations (ICTAI)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Technological Advancements and Innovations (ICTAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI53825.2021.9673159\",\"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 International Conference on Technological Advancements and Innovations (ICTAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI53825.2021.9673159","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

“然而,深度学习网络需要大量的数据,并且由于某些植物病变数据难以获取且具有相似的结构,因此深度学习最近在植物病变识别方面显示出潜力”,必须通过生成完整的植物病变叶片图片来增加数据集。针对这一问题,本文对植物病变叶片全貌和罕见图像的生成技术进行了综述,以期提高分类网络的准确性。本文研究的一些好处是对基于氮化镓的植物病害鉴定进行了系统的调查,许多作者给出了理论和实际实施。我的方法已被证明可以成功地扩展植物病变研究,并在未来提高分类网络的识别精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A Systematic Literature Survey on Generative Adversarial Network Based Crop Disease Identification
"However, a deep learning network requires a large amount of data, and because certain plant lesion data is difficult to acquire and has a similar structure, deep learning has lately showed potential in the identification of plant lesions.", The data set has to be increased by generating full plant lesion leaf pictures. To address this issue, this article offers a survey on technique for generating full and rare picture of plant lesion leaf that may be enhance the accuracy of classification network. Some of the benefits of our research in this article are a systematic survey on GAN based plant disease identification where many authors gave the theory and practical implementation on that. My approach has been shown to successfully extend plant lesion research and improve the classification network’s identification accuracy in the future.
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
期刊最新文献
Malware Detection Using Machine Learning Prediction of Students’ Perceptions towards Technology’ Benefits, Use and Development Dynamic Time Tracking and Task Monitoring Agent Service A Systematic Literature Survey on Generative Adversarial Network Based Crop Disease Identification Study of Convective Heat Transfer Characteristics of Nano Fluids in Circular Tube
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1