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A mobile-based system for maize plant leaf disease detection and classification using deep learning
Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10226393/ https://www.ncbi.nlm.nih.gov/pubmed/37255561 http://dx.doi.org/10.3389/fpls.2023.1079366 |
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author | Khan, Faiza Zafar, Noureen Tahir, Muhammad Naveed Aqib, Muhammad Waheed, Hamna Haroon, Zainab |
author_facet | Khan, Faiza Zafar, Noureen Tahir, Muhammad Naveed Aqib, Muhammad Waheed, Hamna Haroon, Zainab |
author_sort | Khan, Faiza |
collection | PubMed |
description | Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds. |
format | Online Article Text |
id | pubmed-10226393 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102263932023-05-30 A mobile-based system for maize plant leaf disease detection and classification using deep learning Khan, Faiza Zafar, Noureen Tahir, Muhammad Naveed Aqib, Muhammad Waheed, Hamna Haroon, Zainab Front Plant Sci Plant Science Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds. Frontiers Media S.A. 2023-05-15 /pmc/articles/PMC10226393/ /pubmed/37255561 http://dx.doi.org/10.3389/fpls.2023.1079366 Text en Copyright © 2023 Khan, Zafar, Tahir, Aqib, Waheed and Haroon 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 Khan, Faiza Zafar, Noureen Tahir, Muhammad Naveed Aqib, Muhammad Waheed, Hamna Haroon, Zainab A mobile-based system for maize plant leaf disease detection and classification using deep learning |
title | A mobile-based system for maize plant leaf disease detection and classification using deep learning |
title_full | A mobile-based system for maize plant leaf disease detection and classification using deep learning |
title_fullStr | A mobile-based system for maize plant leaf disease detection and classification using deep learning |
title_full_unstemmed | A mobile-based system for maize plant leaf disease detection and classification using deep learning |
title_short | A mobile-based system for maize plant leaf disease detection and classification using deep learning |
title_sort | mobile-based system for maize plant leaf disease detection and classification using deep learning |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10226393/ https://www.ncbi.nlm.nih.gov/pubmed/37255561 http://dx.doi.org/10.3389/fpls.2023.1079366 |
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