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Identification of plant leaf diseases by deep learning based on channel attention and channel pruning
Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9686387/ https://www.ncbi.nlm.nih.gov/pubmed/36438120 http://dx.doi.org/10.3389/fpls.2022.1023515 |
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author | Chen, Riyao Qi, Haixia Liang, Yu Yang, Mingchao |
author_facet | Chen, Riyao Qi, Haixia Liang, Yu Yang, Mingchao |
author_sort | Chen, Riyao |
collection | PubMed |
description | Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification, but such approaches still suffer from low identification accuracy and numerous parameters. Hence, this paper proposes a model combining channel attention and channel pruning called CACPNET, suitable for disease identification of common species. The channel attention mechanism adopts a local cross-channel strategy without dimensionality reduction, which is inserted into a ResNet-18-based model that combines global average pooling with global max pooling to effectively improve the features’ extracting ability of plant leaf diseases. Based on the model’s optimum feature extraction condition, unimportant channels are removed to reduce the model’s parameters and complexity via the L1-norm channel weight and local compression ratio. The accuracy of CACPNET on the public dataset PlantVillage reaches 99.7% and achieves 97.7% on the local peanut leaf disease dataset. Compared with the base ResNet-18 model, the floating point operations (FLOPs) decreased by 30.35%, the parameters by 57.97%, the model size by 57.85%, and the GPU RAM requirements by 8.3%. Additionally, CACPNET outperforms current models considering inference time and throughput, reaching 22.8 ms/frame and 75.5 frames/s, respectively. The results outline that CACPNET is appealing for deployment on edge devices to improve the efficiency of precision agriculture in plant disease detection. |
format | Online Article Text |
id | pubmed-9686387 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96863872022-11-25 Identification of plant leaf diseases by deep learning based on channel attention and channel pruning Chen, Riyao Qi, Haixia Liang, Yu Yang, Mingchao Front Plant Sci Plant Science Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification, but such approaches still suffer from low identification accuracy and numerous parameters. Hence, this paper proposes a model combining channel attention and channel pruning called CACPNET, suitable for disease identification of common species. The channel attention mechanism adopts a local cross-channel strategy without dimensionality reduction, which is inserted into a ResNet-18-based model that combines global average pooling with global max pooling to effectively improve the features’ extracting ability of plant leaf diseases. Based on the model’s optimum feature extraction condition, unimportant channels are removed to reduce the model’s parameters and complexity via the L1-norm channel weight and local compression ratio. The accuracy of CACPNET on the public dataset PlantVillage reaches 99.7% and achieves 97.7% on the local peanut leaf disease dataset. Compared with the base ResNet-18 model, the floating point operations (FLOPs) decreased by 30.35%, the parameters by 57.97%, the model size by 57.85%, and the GPU RAM requirements by 8.3%. Additionally, CACPNET outperforms current models considering inference time and throughput, reaching 22.8 ms/frame and 75.5 frames/s, respectively. The results outline that CACPNET is appealing for deployment on edge devices to improve the efficiency of precision agriculture in plant disease detection. Frontiers Media S.A. 2022-11-10 /pmc/articles/PMC9686387/ /pubmed/36438120 http://dx.doi.org/10.3389/fpls.2022.1023515 Text en Copyright © 2022 Chen, Qi, Liang and Yang 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 Chen, Riyao Qi, Haixia Liang, Yu Yang, Mingchao Identification of plant leaf diseases by deep learning based on channel attention and channel pruning |
title | Identification of plant leaf diseases by deep learning based on channel attention and channel pruning |
title_full | Identification of plant leaf diseases by deep learning based on channel attention and channel pruning |
title_fullStr | Identification of plant leaf diseases by deep learning based on channel attention and channel pruning |
title_full_unstemmed | Identification of plant leaf diseases by deep learning based on channel attention and channel pruning |
title_short | Identification of plant leaf diseases by deep learning based on channel attention and channel pruning |
title_sort | identification of plant leaf diseases by deep learning based on channel attention and channel pruning |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9686387/ https://www.ncbi.nlm.nih.gov/pubmed/36438120 http://dx.doi.org/10.3389/fpls.2022.1023515 |
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