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Tomato Pest Recognition Algorithm Based on Improved YOLOv4
Tomato plants are infected by diseases and insect pests in the growth process, which will lead to a reduction in tomato production and economic benefits for growers. At present, tomato pests are detected mainly through manual collection and classification of field samples by professionals. This manu...
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/PMC9326248/ https://www.ncbi.nlm.nih.gov/pubmed/35909759 http://dx.doi.org/10.3389/fpls.2022.814681 |
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author | Liu, Jun Wang, Xuewei Miao, Wenqing Liu, Guoxu |
author_facet | Liu, Jun Wang, Xuewei Miao, Wenqing Liu, Guoxu |
author_sort | Liu, Jun |
collection | PubMed |
description | Tomato plants are infected by diseases and insect pests in the growth process, which will lead to a reduction in tomato production and economic benefits for growers. At present, tomato pests are detected mainly through manual collection and classification of field samples by professionals. This manual classification method is expensive and time-consuming. The existing automatic pest detection methods based on a computer require a simple background environment of the pests and cannot locate pests. To solve these problems, based on the idea of deep learning, a tomato pest identification algorithm based on an improved YOLOv4 fusing triplet attention mechanism (YOLOv4-TAM) was proposed, and the problem of imbalances in the number of positive and negative samples in the image was addressed by introducing a focal loss function. The K-means + + clustering algorithm is used to obtain a set of anchor boxes that correspond to the pest dataset. At the same time, a labeled dataset of tomato pests was established. The proposed algorithm was tested on the established dataset, and the average recognition accuracy reached 95.2%. The experimental results show that the proposed method can effectively improve the accuracy of tomato pests, which is superior to the previous methods. Algorithmic performance on practical images of healthy and unhealthy objects shows that the proposed method is feasible for the detection of tomato pests. |
format | Online Article Text |
id | pubmed-9326248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93262482022-07-28 Tomato Pest Recognition Algorithm Based on Improved YOLOv4 Liu, Jun Wang, Xuewei Miao, Wenqing Liu, Guoxu Front Plant Sci Plant Science Tomato plants are infected by diseases and insect pests in the growth process, which will lead to a reduction in tomato production and economic benefits for growers. At present, tomato pests are detected mainly through manual collection and classification of field samples by professionals. This manual classification method is expensive and time-consuming. The existing automatic pest detection methods based on a computer require a simple background environment of the pests and cannot locate pests. To solve these problems, based on the idea of deep learning, a tomato pest identification algorithm based on an improved YOLOv4 fusing triplet attention mechanism (YOLOv4-TAM) was proposed, and the problem of imbalances in the number of positive and negative samples in the image was addressed by introducing a focal loss function. The K-means + + clustering algorithm is used to obtain a set of anchor boxes that correspond to the pest dataset. At the same time, a labeled dataset of tomato pests was established. The proposed algorithm was tested on the established dataset, and the average recognition accuracy reached 95.2%. The experimental results show that the proposed method can effectively improve the accuracy of tomato pests, which is superior to the previous methods. Algorithmic performance on practical images of healthy and unhealthy objects shows that the proposed method is feasible for the detection of tomato pests. Frontiers Media S.A. 2022-07-13 /pmc/articles/PMC9326248/ /pubmed/35909759 http://dx.doi.org/10.3389/fpls.2022.814681 Text en Copyright © 2022 Liu, Wang, Miao and Liu. 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 Liu, Jun Wang, Xuewei Miao, Wenqing Liu, Guoxu Tomato Pest Recognition Algorithm Based on Improved YOLOv4 |
title | Tomato Pest Recognition Algorithm Based on Improved YOLOv4 |
title_full | Tomato Pest Recognition Algorithm Based on Improved YOLOv4 |
title_fullStr | Tomato Pest Recognition Algorithm Based on Improved YOLOv4 |
title_full_unstemmed | Tomato Pest Recognition Algorithm Based on Improved YOLOv4 |
title_short | Tomato Pest Recognition Algorithm Based on Improved YOLOv4 |
title_sort | tomato pest recognition algorithm based on improved yolov4 |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9326248/ https://www.ncbi.nlm.nih.gov/pubmed/35909759 http://dx.doi.org/10.3389/fpls.2022.814681 |
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