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An advanced deep learning models-based plant disease detection: A review of recent research

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preve...

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Autores principales: Shoaib, Muhammad, Shah, Babar, EI-Sappagh, Shaker, Ali, Akhtar, Ullah, Asad, Alenezi, Fayadh, Gechev, Tsanko, Hussain, Tariq, Ali, Farman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10070872/
https://www.ncbi.nlm.nih.gov/pubmed/37025141
http://dx.doi.org/10.3389/fpls.2023.1158933
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author Shoaib, Muhammad
Shah, Babar
EI-Sappagh, Shaker
Ali, Akhtar
Ullah, Asad
Alenezi, Fayadh
Gechev, Tsanko
Hussain, Tariq
Ali, Farman
author_facet Shoaib, Muhammad
Shah, Babar
EI-Sappagh, Shaker
Ali, Akhtar
Ullah, Asad
Alenezi, Fayadh
Gechev, Tsanko
Hussain, Tariq
Ali, Farman
author_sort Shoaib, Muhammad
collection PubMed
description Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.
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spelling pubmed-100708722023-04-05 An advanced deep learning models-based plant disease detection: A review of recent research Shoaib, Muhammad Shah, Babar EI-Sappagh, Shaker Ali, Akhtar Ullah, Asad Alenezi, Fayadh Gechev, Tsanko Hussain, Tariq Ali, Farman Front Plant Sci Plant Science Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation. Frontiers Media S.A. 2023-03-21 /pmc/articles/PMC10070872/ /pubmed/37025141 http://dx.doi.org/10.3389/fpls.2023.1158933 Text en Copyright © 2023 Shoaib, Shah, EI-Sappagh, Ali, Ullah, Alenezi, Gechev, Hussain and Ali https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Shoaib, Muhammad
Shah, Babar
EI-Sappagh, Shaker
Ali, Akhtar
Ullah, Asad
Alenezi, Fayadh
Gechev, Tsanko
Hussain, Tariq
Ali, Farman
An advanced deep learning models-based plant disease detection: A review of recent research
title An advanced deep learning models-based plant disease detection: A review of recent research
title_full An advanced deep learning models-based plant disease detection: A review of recent research
title_fullStr An advanced deep learning models-based plant disease detection: A review of recent research
title_full_unstemmed An advanced deep learning models-based plant disease detection: A review of recent research
title_short An advanced deep learning models-based plant disease detection: A review of recent research
title_sort advanced deep learning models-based plant disease detection: a review of recent research
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10070872/
https://www.ncbi.nlm.nih.gov/pubmed/37025141
http://dx.doi.org/10.3389/fpls.2023.1158933
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