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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...

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Autores principales: Saleem, Muhammad Hammad, Khanchi, Sapna, Potgieter, Johan, Arif, Khalid Mahmood
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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.
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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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