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An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model
INTRODUCTION: Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to...
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/PMC10600380/ https://www.ncbi.nlm.nih.gov/pubmed/37900756 http://dx.doi.org/10.3389/fpls.2023.1212747 |
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author | Ullah, Naeem Khan, Javed Ali Almakdi, Sultan Alshehri, Mohammed S. Al Qathrady, Mimonah El-Rashidy, Nora El-Sappagh, Shaker Ali, Farman |
author_facet | Ullah, Naeem Khan, Javed Ali Almakdi, Sultan Alshehri, Mohammed S. Al Qathrady, Mimonah El-Rashidy, Nora El-Sappagh, Shaker Ali, Farman |
author_sort | Ullah, Naeem |
collection | PubMed |
description | INTRODUCTION: Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. METHOD: This research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications. RESULTS: The proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. DISCUSSION: The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases. |
format | Online Article Text |
id | pubmed-10600380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106003802023-10-27 An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model Ullah, Naeem Khan, Javed Ali Almakdi, Sultan Alshehri, Mohammed S. Al Qathrady, Mimonah El-Rashidy, Nora El-Sappagh, Shaker Ali, Farman Front Plant Sci Plant Science INTRODUCTION: Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. METHOD: This research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications. RESULTS: The proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. DISCUSSION: The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases. Frontiers Media S.A. 2023-10-11 /pmc/articles/PMC10600380/ /pubmed/37900756 http://dx.doi.org/10.3389/fpls.2023.1212747 Text en Copyright © 2023 Ullah, Khan, Almakdi, Alshehri, Al Qathrady, El-Rashidy, El-Sappagh and Ali 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 Ullah, Naeem Khan, Javed Ali Almakdi, Sultan Alshehri, Mohammed S. Al Qathrady, Mimonah El-Rashidy, Nora El-Sappagh, Shaker Ali, Farman An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model |
title | An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model |
title_full | An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model |
title_fullStr | An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model |
title_full_unstemmed | An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model |
title_short | An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model |
title_sort | effective approach for plant leaf diseases classification based on a novel deepplantnet deep learning model |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600380/ https://www.ncbi.nlm.nih.gov/pubmed/37900756 http://dx.doi.org/10.3389/fpls.2023.1212747 |
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