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Weed25: A deep learning dataset for weed identification
Weed suppression is an important factor affecting crop yields. Precise identification of weed species will contribute to automatic weeding by applying proper herbicides, hoeing position determination, and hoeing depth to specific plants as well as reducing crop injury. However, the lack of datasets...
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/PMC9748680/ https://www.ncbi.nlm.nih.gov/pubmed/36531369 http://dx.doi.org/10.3389/fpls.2022.1053329 |
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author | Wang, Pei Tang, Yin Luo, Fan Wang, Lihong Li, Chengsong Niu, Qi Li, Hui |
author_facet | Wang, Pei Tang, Yin Luo, Fan Wang, Lihong Li, Chengsong Niu, Qi Li, Hui |
author_sort | Wang, Pei |
collection | PubMed |
description | Weed suppression is an important factor affecting crop yields. Precise identification of weed species will contribute to automatic weeding by applying proper herbicides, hoeing position determination, and hoeing depth to specific plants as well as reducing crop injury. However, the lack of datasets of weeds in the field has limited the application of deep learning techniques in weed management. In this paper, it presented a dataset of weeds in fields, Weed25, which contained 14,035 images of 25 different weed species. Both monocot and dicot weed image resources were included in this dataset. Meanwhile, weed images at different growth stages were also recorded. Several common deep learning detection models—YOLOv3, YOLOv5, and Faster R-CNN—were applied for weed identification model training using this dataset. The results showed that the average accuracy of detection under the same training parameters were 91.8%, 92.4%, and 92.15% respectively. It presented that Weed25 could be a potential effective training resource for further development of in-field real-time weed identification models. The dataset is available at https://pan.baidu.com/s/1rnUoDm7IxxmX1n1LmtXNXw; the password is rn5h. |
format | Online Article Text |
id | pubmed-9748680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97486802022-12-15 Weed25: A deep learning dataset for weed identification Wang, Pei Tang, Yin Luo, Fan Wang, Lihong Li, Chengsong Niu, Qi Li, Hui Front Plant Sci Plant Science Weed suppression is an important factor affecting crop yields. Precise identification of weed species will contribute to automatic weeding by applying proper herbicides, hoeing position determination, and hoeing depth to specific plants as well as reducing crop injury. However, the lack of datasets of weeds in the field has limited the application of deep learning techniques in weed management. In this paper, it presented a dataset of weeds in fields, Weed25, which contained 14,035 images of 25 different weed species. Both monocot and dicot weed image resources were included in this dataset. Meanwhile, weed images at different growth stages were also recorded. Several common deep learning detection models—YOLOv3, YOLOv5, and Faster R-CNN—were applied for weed identification model training using this dataset. The results showed that the average accuracy of detection under the same training parameters were 91.8%, 92.4%, and 92.15% respectively. It presented that Weed25 could be a potential effective training resource for further development of in-field real-time weed identification models. The dataset is available at https://pan.baidu.com/s/1rnUoDm7IxxmX1n1LmtXNXw; the password is rn5h. Frontiers Media S.A. 2022-11-30 /pmc/articles/PMC9748680/ /pubmed/36531369 http://dx.doi.org/10.3389/fpls.2022.1053329 Text en Copyright © 2022 Wang, Tang, Luo, Wang, Li, Niu and Li 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 Wang, Pei Tang, Yin Luo, Fan Wang, Lihong Li, Chengsong Niu, Qi Li, Hui Weed25: A deep learning dataset for weed identification |
title | Weed25: A deep learning dataset for weed identification |
title_full | Weed25: A deep learning dataset for weed identification |
title_fullStr | Weed25: A deep learning dataset for weed identification |
title_full_unstemmed | Weed25: A deep learning dataset for weed identification |
title_short | Weed25: A deep learning dataset for weed identification |
title_sort | weed25: a deep learning dataset for weed identification |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748680/ https://www.ncbi.nlm.nih.gov/pubmed/36531369 http://dx.doi.org/10.3389/fpls.2022.1053329 |
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