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A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework
The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food securit...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049121/ https://www.ncbi.nlm.nih.gov/pubmed/33954231 http://dx.doi.org/10.7717/peerj-cs.432 |
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author | Bari, Bifta Sama Islam, Md Nahidul Rashid, Mamunur Hasan, Md Jahid Razman, Mohd Azraai Mohd Musa, Rabiu Muazu Ab Nasir, Ahmad Fakhri P.P. Abdul Majeed, Anwar |
author_facet | Bari, Bifta Sama Islam, Md Nahidul Rashid, Mamunur Hasan, Md Jahid Razman, Mohd Azraai Mohd Musa, Rabiu Muazu Ab Nasir, Ahmad Fakhri P.P. Abdul Majeed, Anwar |
author_sort | Bari, Bifta Sama |
collection | PubMed |
description | The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time. |
format | Online Article Text |
id | pubmed-8049121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80491212021-05-04 A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework Bari, Bifta Sama Islam, Md Nahidul Rashid, Mamunur Hasan, Md Jahid Razman, Mohd Azraai Mohd Musa, Rabiu Muazu Ab Nasir, Ahmad Fakhri P.P. Abdul Majeed, Anwar PeerJ Comput Sci Artificial Intelligence The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time. PeerJ Inc. 2021-04-07 /pmc/articles/PMC8049121/ /pubmed/33954231 http://dx.doi.org/10.7717/peerj-cs.432 Text en © 2021 Bari et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Bari, Bifta Sama Islam, Md Nahidul Rashid, Mamunur Hasan, Md Jahid Razman, Mohd Azraai Mohd Musa, Rabiu Muazu Ab Nasir, Ahmad Fakhri P.P. Abdul Majeed, Anwar A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework |
title | A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework |
title_full | A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework |
title_fullStr | A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework |
title_full_unstemmed | A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework |
title_short | A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework |
title_sort | real-time approach of diagnosing rice leaf disease using deep learning-based faster r-cnn framework |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049121/ https://www.ncbi.nlm.nih.gov/pubmed/33954231 http://dx.doi.org/10.7717/peerj-cs.432 |
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