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A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identificat...

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Autores principales: Shoaib, Muhammad, Shah, Babar, Hussain, Tariq, Ali, Akhtar, Ullah, Asad, Alenezi, Fayadh, Gechev, Tsanko, Ali, Farman, Syed, Ikram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798446/
https://www.ncbi.nlm.nih.gov/pubmed/36589071
http://dx.doi.org/10.3389/fpls.2022.1095547
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author Shoaib, Muhammad
Shah, Babar
Hussain, Tariq
Ali, Akhtar
Ullah, Asad
Alenezi, Fayadh
Gechev, Tsanko
Ali, Farman
Syed, Ikram
author_facet Shoaib, Muhammad
Shah, Babar
Hussain, Tariq
Ali, Akhtar
Ullah, Asad
Alenezi, Fayadh
Gechev, Tsanko
Ali, Farman
Syed, Ikram
author_sort Shoaib, Muhammad
collection PubMed
description Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant’s survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.
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spelling pubmed-97984462022-12-30 A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation Shoaib, Muhammad Shah, Babar Hussain, Tariq Ali, Akhtar Ullah, Asad Alenezi, Fayadh Gechev, Tsanko Ali, Farman Syed, Ikram Front Plant Sci Plant Science Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant’s survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield. Frontiers Media S.A. 2022-12-15 /pmc/articles/PMC9798446/ /pubmed/36589071 http://dx.doi.org/10.3389/fpls.2022.1095547 Text en Copyright © 2022 Shoaib, Shah, Hussain, Ali, Ullah, Alenezi, Gechev, Ali and Syed 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
Hussain, Tariq
Ali, Akhtar
Ullah, Asad
Alenezi, Fayadh
Gechev, Tsanko
Ali, Farman
Syed, Ikram
A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation
title A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation
title_full A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation
title_fullStr A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation
title_full_unstemmed A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation
title_short A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation
title_sort deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798446/
https://www.ncbi.nlm.nih.gov/pubmed/36589071
http://dx.doi.org/10.3389/fpls.2022.1095547
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