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Image-Based Plant Disease Identification by Deep Learning Meta-Architectures
The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7692455/ https://www.ncbi.nlm.nih.gov/pubmed/33121188 http://dx.doi.org/10.3390/plants9111451 |
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author | Saleem, Muhammad Hammad Khanchi, Sapna Potgieter, Johan Arif, Khalid Mahmood |
author_facet | Saleem, Muhammad Hammad Khanchi, Sapna Potgieter, Johan Arif, Khalid Mahmood |
author_sort | Saleem, Muhammad Hammad |
collection | PubMed |
description | The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment. |
format | Online Article Text |
id | pubmed-7692455 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76924552020-11-28 Image-Based Plant Disease Identification by Deep Learning Meta-Architectures Saleem, Muhammad Hammad Khanchi, Sapna Potgieter, Johan Arif, Khalid Mahmood Plants (Basel) Article The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment. MDPI 2020-10-27 /pmc/articles/PMC7692455/ /pubmed/33121188 http://dx.doi.org/10.3390/plants9111451 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Saleem, Muhammad Hammad Khanchi, Sapna Potgieter, Johan Arif, Khalid Mahmood Image-Based Plant Disease Identification by Deep Learning Meta-Architectures |
title | Image-Based Plant Disease Identification by Deep Learning Meta-Architectures |
title_full | Image-Based Plant Disease Identification by Deep Learning Meta-Architectures |
title_fullStr | Image-Based Plant Disease Identification by Deep Learning Meta-Architectures |
title_full_unstemmed | Image-Based Plant Disease Identification by Deep Learning Meta-Architectures |
title_short | Image-Based Plant Disease Identification by Deep Learning Meta-Architectures |
title_sort | image-based plant disease identification by deep learning meta-architectures |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7692455/ https://www.ncbi.nlm.nih.gov/pubmed/33121188 http://dx.doi.org/10.3390/plants9111451 |
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