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WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion
Accurate and dependable weed detection technology is a prerequisite for weed control robots to do autonomous weeding. Due to the complexity of the farmland environment and the resemblance between crops and weeds, detecting weeds in the field under natural settings is a difficult task. Existing deep...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408303/ https://www.ncbi.nlm.nih.gov/pubmed/37560032 http://dx.doi.org/10.3389/fpls.2023.1226329 |
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author | Guo, Zhiqiang Goh, Hui Hwang Li, Xiuhua Zhang, Muqing Li, Yong |
author_facet | Guo, Zhiqiang Goh, Hui Hwang Li, Xiuhua Zhang, Muqing Li, Yong |
author_sort | Guo, Zhiqiang |
collection | PubMed |
description | Accurate and dependable weed detection technology is a prerequisite for weed control robots to do autonomous weeding. Due to the complexity of the farmland environment and the resemblance between crops and weeds, detecting weeds in the field under natural settings is a difficult task. Existing deep learning-based weed detection approaches often suffer from issues such as monotonous detection scene, lack of picture samples and location information for detected items, low detection accuracy, etc. as compared to conventional weed detection methods. To address these issues, WeedNet-R, a vision-based network for weed identification and localization in sugar beet fields, is proposed. WeedNet-R adds numerous context modules to RetinaNet’s neck in order to combine context information from many feature maps and so expand the effective receptive fields of the entire network. During model training, meantime, a learning rate adjustment method combining an untuned exponential warmup schedule and cosine annealing technique is implemented. As a result, the suggested method for weed detection is more accurate without requiring a considerable increase in model parameters. The WeedNet-R was trained and assessed using the OD-SugarBeets dataset, which is enhanced by manually adding the bounding box labels based on the publicly available agricultural dataset, i.e. SugarBeet2016. Compared to the original RetinaNet, the mAP of the proposed WeedNet-R increased in the weed detection job in sugar beet fields by 4.65% to 92.30%. WeedNet-R’s average precision for weed and sugar beet is 85.70% and 98.89%, respectively. WeedNet-R outperforms other sophisticated object detection algorithms in terms of detection accuracy while matching other single-stage detectors in terms of detection speed. |
format | Online Article Text |
id | pubmed-10408303 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104083032023-08-09 WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion Guo, Zhiqiang Goh, Hui Hwang Li, Xiuhua Zhang, Muqing Li, Yong Front Plant Sci Plant Science Accurate and dependable weed detection technology is a prerequisite for weed control robots to do autonomous weeding. Due to the complexity of the farmland environment and the resemblance between crops and weeds, detecting weeds in the field under natural settings is a difficult task. Existing deep learning-based weed detection approaches often suffer from issues such as monotonous detection scene, lack of picture samples and location information for detected items, low detection accuracy, etc. as compared to conventional weed detection methods. To address these issues, WeedNet-R, a vision-based network for weed identification and localization in sugar beet fields, is proposed. WeedNet-R adds numerous context modules to RetinaNet’s neck in order to combine context information from many feature maps and so expand the effective receptive fields of the entire network. During model training, meantime, a learning rate adjustment method combining an untuned exponential warmup schedule and cosine annealing technique is implemented. As a result, the suggested method for weed detection is more accurate without requiring a considerable increase in model parameters. The WeedNet-R was trained and assessed using the OD-SugarBeets dataset, which is enhanced by manually adding the bounding box labels based on the publicly available agricultural dataset, i.e. SugarBeet2016. Compared to the original RetinaNet, the mAP of the proposed WeedNet-R increased in the weed detection job in sugar beet fields by 4.65% to 92.30%. WeedNet-R’s average precision for weed and sugar beet is 85.70% and 98.89%, respectively. WeedNet-R outperforms other sophisticated object detection algorithms in terms of detection accuracy while matching other single-stage detectors in terms of detection speed. Frontiers Media S.A. 2023-07-24 /pmc/articles/PMC10408303/ /pubmed/37560032 http://dx.doi.org/10.3389/fpls.2023.1226329 Text en Copyright © 2023 Guo, Goh, Li, Zhang 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 Guo, Zhiqiang Goh, Hui Hwang Li, Xiuhua Zhang, Muqing Li, Yong WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion |
title | WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion |
title_full | WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion |
title_fullStr | WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion |
title_full_unstemmed | WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion |
title_short | WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion |
title_sort | weednet-r: a sugar beet field weed detection algorithm based on enhanced retinanet and context semantic fusion |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408303/ https://www.ncbi.nlm.nih.gov/pubmed/37560032 http://dx.doi.org/10.3389/fpls.2023.1226329 |
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