植物物种识别的深度学习和提供作为一个移动应用

M. F. Adak
{"title":"植物物种识别的深度学习和提供作为一个移动应用","authors":"M. F. Adak","doi":"10.35377/saucis.vi.773465","DOIUrl":null,"url":null,"abstract":"Image processing techniques give highly successful results when used deep learning in classification studies. Applications benefit from this kind of work to make life easier. In this study, a mobile application is developed that takes photo of a plant and makes image processing on it to provide information about its name, the time to change the soil, the amount of sun light and nutrition it needs. The model is trained using the Convolutional Neural Networks, and dataset is successfully applied to the network. Currently, the application is capable to classify 43 different plants in mobile environment, and its classification capacity is planned to be expanded with new plant species as a future study. Up to 90% accuracy is reached in this study with the current version of the application.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Identification of Plant Species by Deep Learning and Providing as A Mobile Application\",\"authors\":\"M. F. Adak\",\"doi\":\"10.35377/saucis.vi.773465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image processing techniques give highly successful results when used deep learning in classification studies. Applications benefit from this kind of work to make life easier. In this study, a mobile application is developed that takes photo of a plant and makes image processing on it to provide information about its name, the time to change the soil, the amount of sun light and nutrition it needs. The model is trained using the Convolutional Neural Networks, and dataset is successfully applied to the network. Currently, the application is capable to classify 43 different plants in mobile environment, and its classification capacity is planned to be expanded with new plant species as a future study. Up to 90% accuracy is reached in this study with the current version of the application.\",\"PeriodicalId\":257636,\"journal\":{\"name\":\"Sakarya University Journal of Computer and Information Sciences\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sakarya University Journal of Computer and Information Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35377/saucis.vi.773465\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sakarya University Journal of Computer and Information Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35377/saucis.vi.773465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

图像处理技术在分类研究中使用深度学习获得了非常成功的结果。应用程序从这种工作中受益,使生活更轻松。在这项研究中,开发了一个移动应用程序,可以拍摄植物的照片并对其进行图像处理,以提供有关其名称,更换土壤的时间,阳光照射量和所需营养的信息。利用卷积神经网络对模型进行训练,并成功地将数据集应用到网络中。目前,该应用程序能够在移动环境中对43种不同的植物进行分类,并计划在未来的研究中扩大其分类能力,增加新的植物物种。在本研究中,使用当前版本的应用程序可达到90%的准确性。
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
Identification of Plant Species by Deep Learning and Providing as A Mobile Application
Image processing techniques give highly successful results when used deep learning in classification studies. Applications benefit from this kind of work to make life easier. In this study, a mobile application is developed that takes photo of a plant and makes image processing on it to provide information about its name, the time to change the soil, the amount of sun light and nutrition it needs. The model is trained using the Convolutional Neural Networks, and dataset is successfully applied to the network. Currently, the application is capable to classify 43 different plants in mobile environment, and its classification capacity is planned to be expanded with new plant species as a future study. Up to 90% accuracy is reached in this study with the current version of the application.
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
Prediction of Cardiovascular Disease Based on Voting Ensemble Model and SHAP Analysis A NOVEL ADDITIVE INTERNET OF THINGS (IoT) FEATURES AND CONVOLUTIONAL NEURAL NETWORK FOR CLASSIFICATION AND SOURCE IDENTIFICATION OF IoT DEVICES High-Capacity Multiplier Design Using Look Up Table Sequential and Correlated Image Hash Code Generation with Deep Reinforcement Learning Price Prediction Using Web Scraping and Machine Learning Algorithms in the Used Car Market
×
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