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Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images
Rice (Oryza sativa) is India's major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using pl...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199794/ https://www.ncbi.nlm.nih.gov/pubmed/37213211 http://dx.doi.org/10.1155/2023/5644727 |
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author | Balaji, V. Anushkannan, N. K. Narahari, Sujatha Canavoy Rattan, Punam Verma, Devvret Awasthi, Deepak Kumar Pandian, A. Anbarasa Veeramanickam, M. R. M. Mulat, Molla Bayih |
author_facet | Balaji, V. Anushkannan, N. K. Narahari, Sujatha Canavoy Rattan, Punam Verma, Devvret Awasthi, Deepak Kumar Pandian, A. Anbarasa Veeramanickam, M. R. M. Mulat, Molla Bayih |
author_sort | Balaji, V. |
collection | PubMed |
description | Rice (Oryza sativa) is India's major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy. |
format | Online Article Text |
id | pubmed-10199794 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-101997942023-05-21 Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images Balaji, V. Anushkannan, N. K. Narahari, Sujatha Canavoy Rattan, Punam Verma, Devvret Awasthi, Deepak Kumar Pandian, A. Anbarasa Veeramanickam, M. R. M. Mulat, Molla Bayih Contrast Media Mol Imaging Research Article Rice (Oryza sativa) is India's major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy. Hindawi 2023-05-13 /pmc/articles/PMC10199794/ /pubmed/37213211 http://dx.doi.org/10.1155/2023/5644727 Text en Copyright © 2023 V. Balaji et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Balaji, V. Anushkannan, N. K. Narahari, Sujatha Canavoy Rattan, Punam Verma, Devvret Awasthi, Deepak Kumar Pandian, A. Anbarasa Veeramanickam, M. R. M. Mulat, Molla Bayih Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images |
title | Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images |
title_full | Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images |
title_fullStr | Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images |
title_full_unstemmed | Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images |
title_short | Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images |
title_sort | deep transfer learning technique for multimodal disease classification in plant images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199794/ https://www.ncbi.nlm.nih.gov/pubmed/37213211 http://dx.doi.org/10.1155/2023/5644727 |
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