基于深度学习框架的番茄叶病生物分类

Q4 Biochemistry, Genetics and Molecular Biology
A. Aggarwal
{"title":"基于深度学习框架的番茄叶病生物分类","authors":"A. Aggarwal","doi":"10.46300/91011.2022.16.30","DOIUrl":null,"url":null,"abstract":"Biological Tomato leaf classification is very important to decide the pesticide, insecticide, and other treatments needed for the plant to yield good crop. The images captured by handheld cameras or using drones are used by various machine learning algorithms to identify the diseases. Such methods need extraction of features from the images before the machine learning methods can be used for disease identification. In this paper, a deep learning framework is proposed that automatically extracts features in a hierarchical manner. The features are classified using neural networks to classify the leaves into three classes, viz. no disease, bacterial spot, and Septoria leaf spot. The performance of the model is tested using accuracy as the performance metric. The obtained performance metric validates the performance of the method. The method is useful for taking corrective measures to disease management of tomato plants.","PeriodicalId":53488,"journal":{"name":"International Journal of Biology and Biomedical Engineering","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Biological Tomato Leaf Disease Classification using Deep Learning Framework\",\"authors\":\"A. Aggarwal\",\"doi\":\"10.46300/91011.2022.16.30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Biological Tomato leaf classification is very important to decide the pesticide, insecticide, and other treatments needed for the plant to yield good crop. The images captured by handheld cameras or using drones are used by various machine learning algorithms to identify the diseases. Such methods need extraction of features from the images before the machine learning methods can be used for disease identification. In this paper, a deep learning framework is proposed that automatically extracts features in a hierarchical manner. The features are classified using neural networks to classify the leaves into three classes, viz. no disease, bacterial spot, and Septoria leaf spot. The performance of the model is tested using accuracy as the performance metric. The obtained performance metric validates the performance of the method. The method is useful for taking corrective measures to disease management of tomato plants.\",\"PeriodicalId\":53488,\"journal\":{\"name\":\"International Journal of Biology and Biomedical Engineering\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Biology and Biomedical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46300/91011.2022.16.30\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Biology and Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46300/91011.2022.16.30","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 7

摘要

生物番茄叶片分类对于决定植物生产优质作物所需的杀虫剂、杀虫剂和其他处理方法非常重要。手持摄像机或无人机拍摄的图像被各种机器学习算法用来识别疾病。在机器学习方法可以用于疾病识别之前,这种方法需要从图像中提取特征。本文提出了一种以分层方式自动提取特征的深度学习框架。利用神经网络将叶片特征分类为三类,即无病、细菌斑和Septoria叶斑。使用精度作为性能度量来测试模型的性能。所获得的性能度量验证了该方法的性能。该方法可用于番茄植株病害管理中的纠正措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Biological Tomato Leaf Disease Classification using Deep Learning Framework
Biological Tomato leaf classification is very important to decide the pesticide, insecticide, and other treatments needed for the plant to yield good crop. The images captured by handheld cameras or using drones are used by various machine learning algorithms to identify the diseases. Such methods need extraction of features from the images before the machine learning methods can be used for disease identification. In this paper, a deep learning framework is proposed that automatically extracts features in a hierarchical manner. The features are classified using neural networks to classify the leaves into three classes, viz. no disease, bacterial spot, and Septoria leaf spot. The performance of the model is tested using accuracy as the performance metric. The obtained performance metric validates the performance of the method. The method is useful for taking corrective measures to disease management of tomato plants.
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
International Journal of Biology and Biomedical Engineering
International Journal of Biology and Biomedical Engineering Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
自引率
0.00%
发文量
42
期刊介绍: Topics: Molecular Dynamics, Biochemistry, Biophysics, Quantum Chemistry, Molecular Biology, Cell Biology, Immunology, Neurophysiology, Genetics, Population Dynamics, Dynamics of Diseases, Bioecology, Epidemiology, Social Dynamics, PhotoBiology, PhotoChemistry, Plant Biology, Microbiology, Immunology, Bioinformatics, Signal Transduction, Environmental Systems, Psychological and Cognitive Systems, Pattern Formation, Evolution, Game Theory and Adaptive Dynamics, Bioengineering, Biotechnolgies, Medical Imaging, Medical Signal Processing, Feedback Control in Biology and Chemistry, Fluid Mechanics and Applications in Biomedicine, Space Medicine and Biology, Nuclear Biology and Medicine.
×
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