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Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images
The detection of plant disease is of vital importance in practical agricultural production. It scrutinizes the plant's growth and health condition and guarantees the regular operation and harvest of the agricultural planting to proceed successfully. In recent decades, the maturation of computer...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178295/ https://www.ncbi.nlm.nih.gov/pubmed/35693164 http://dx.doi.org/10.3389/fpls.2022.875693 |
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author | Zhang, Yan Wa, Shiyun Zhang, Longxiang Lv, Chunli |
author_facet | Zhang, Yan Wa, Shiyun Zhang, Longxiang Lv, Chunli |
author_sort | Zhang, Yan |
collection | PubMed |
description | The detection of plant disease is of vital importance in practical agricultural production. It scrutinizes the plant's growth and health condition and guarantees the regular operation and harvest of the agricultural planting to proceed successfully. In recent decades, the maturation of computer vision technology has provided more possibilities for implementing plant disease detection. Nonetheless, detecting plant diseases is typically hindered by factors such as variations in the illuminance and weather when capturing images and the number of leaves or organs containing diseases in one image. Meanwhile, traditional deep learning-based algorithms attain multiple deficiencies in the area of this research: (1) Training models necessitate a significant investment in hardware and a large amount of data. (2) Due to their slow inference speed, models are tough to acclimate to practical production. (3) Models are unable to generalize well enough. Provided these impediments, this study suggested a Tranvolution detection network with GAN modules for plant disease detection. Foremost, a generative model was added ahead of the backbone, and GAN models were added to the attention extraction module to construct GAN modules. Afterward, the Transformer was modified and incorporated with the CNN, and then we suggested the Tranvolution architecture. Eventually, we validated the performance of different generative models' combinations. Experimental outcomes demonstrated that the proposed method satisfyingly achieved 51.7% (Precision), 48.1% (Recall), and 50.3% (mAP), respectively. Furthermore, the SAGAN model was the best in the attention extraction module, while WGAN performed best in image augmentation. Additionally, we deployed the proposed model on Hbird E203 and devised an intelligent agricultural robot to put the model into practical agricultural use. |
format | Online Article Text |
id | pubmed-9178295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91782952022-06-10 Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images Zhang, Yan Wa, Shiyun Zhang, Longxiang Lv, Chunli Front Plant Sci Plant Science The detection of plant disease is of vital importance in practical agricultural production. It scrutinizes the plant's growth and health condition and guarantees the regular operation and harvest of the agricultural planting to proceed successfully. In recent decades, the maturation of computer vision technology has provided more possibilities for implementing plant disease detection. Nonetheless, detecting plant diseases is typically hindered by factors such as variations in the illuminance and weather when capturing images and the number of leaves or organs containing diseases in one image. Meanwhile, traditional deep learning-based algorithms attain multiple deficiencies in the area of this research: (1) Training models necessitate a significant investment in hardware and a large amount of data. (2) Due to their slow inference speed, models are tough to acclimate to practical production. (3) Models are unable to generalize well enough. Provided these impediments, this study suggested a Tranvolution detection network with GAN modules for plant disease detection. Foremost, a generative model was added ahead of the backbone, and GAN models were added to the attention extraction module to construct GAN modules. Afterward, the Transformer was modified and incorporated with the CNN, and then we suggested the Tranvolution architecture. Eventually, we validated the performance of different generative models' combinations. Experimental outcomes demonstrated that the proposed method satisfyingly achieved 51.7% (Precision), 48.1% (Recall), and 50.3% (mAP), respectively. Furthermore, the SAGAN model was the best in the attention extraction module, while WGAN performed best in image augmentation. Additionally, we deployed the proposed model on Hbird E203 and devised an intelligent agricultural robot to put the model into practical agricultural use. Frontiers Media S.A. 2022-05-26 /pmc/articles/PMC9178295/ /pubmed/35693164 http://dx.doi.org/10.3389/fpls.2022.875693 Text en Copyright © 2022 Zhang, Wa, Zhang and Lv. 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 Zhang, Yan Wa, Shiyun Zhang, Longxiang Lv, Chunli Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images |
title | Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images |
title_full | Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images |
title_fullStr | Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images |
title_full_unstemmed | Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images |
title_short | Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images |
title_sort | automatic plant disease detection based on tranvolution detection network with gan modules using leaf images |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178295/ https://www.ncbi.nlm.nih.gov/pubmed/35693164 http://dx.doi.org/10.3389/fpls.2022.875693 |
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