Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images
Abstract The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cai, Weiwei [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 80(2021), 21-23 vom: 24. Juli, Seite 32131-32147 |
|---|---|
| Übergeordnetes Werk: |
volume:80 ; year:2021 ; number:21-23 ; day:24 ; month:07 ; pages:32131-32147 |
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| DOI / URN: |
10.1007/s11042-021-11203-5 |
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OLC2077131756 |
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| 520 | |a Abstract The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold convolution (RCTC) for segmentation of wheat plantation rows. The network is achieved by replacing the AveragePooling of the improved ResNet34 with Capsule. Since the capsule network represents the features by vectors, it can explain the direction of features and the relative positions between features. Therefore, deeper feature information can be extracted. In addition, to reduce redundant features and enhance effective features, a new threshold convolution is also proposed. Experiments on the wheat field dataset show that our proposed algorithm can effectively segment the wheat plantation rows images collected by UAV, and is superior to some existing well-known algorithms, and can provide scientific support and reference for the decision-making process of smart agriculture. | ||
| 650 | 4 | |a Threshold convolution | |
| 650 | 4 | |a Residual-capsule networks | |
| 650 | 4 | |a Wheat plantation rows | |
| 650 | 4 | |a Image segmentation | |
| 700 | 1 | |a Wei, Zhanguo |0 (orcid)0000-0001-9736-502X |4 aut | |
| 700 | 1 | |a Song, Yaping |4 aut | |
| 700 | 1 | |a Li, Meilin |4 aut | |
| 700 | 1 | |a Yang, Xuechun |4 aut | |
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10.1007/s11042-021-11203-5 doi (DE-627)OLC2077131756 (DE-He213)s11042-021-11203-5-p DE-627 ger DE-627 rakwb eng 070 004 VZ Cai, Weiwei verfasserin aut Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold convolution (RCTC) for segmentation of wheat plantation rows. The network is achieved by replacing the AveragePooling of the improved ResNet34 with Capsule. Since the capsule network represents the features by vectors, it can explain the direction of features and the relative positions between features. Therefore, deeper feature information can be extracted. In addition, to reduce redundant features and enhance effective features, a new threshold convolution is also proposed. Experiments on the wheat field dataset show that our proposed algorithm can effectively segment the wheat plantation rows images collected by UAV, and is superior to some existing well-known algorithms, and can provide scientific support and reference for the decision-making process of smart agriculture. Threshold convolution Residual-capsule networks Wheat plantation rows Image segmentation Wei, Zhanguo (orcid)0000-0001-9736-502X aut Song, Yaping aut Li, Meilin aut Yang, Xuechun aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2021), 21-23 vom: 24. Juli, Seite 32131-32147 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2021 number:21-23 day:24 month:07 pages:32131-32147 https://doi.org/10.1007/s11042-021-11203-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW SSG-OLC-PHA SSG-OLC-DE-84 AR 80 2021 21-23 24 07 32131-32147 |
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10.1007/s11042-021-11203-5 doi (DE-627)OLC2077131756 (DE-He213)s11042-021-11203-5-p DE-627 ger DE-627 rakwb eng 070 004 VZ Cai, Weiwei verfasserin aut Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold convolution (RCTC) for segmentation of wheat plantation rows. The network is achieved by replacing the AveragePooling of the improved ResNet34 with Capsule. Since the capsule network represents the features by vectors, it can explain the direction of features and the relative positions between features. Therefore, deeper feature information can be extracted. In addition, to reduce redundant features and enhance effective features, a new threshold convolution is also proposed. Experiments on the wheat field dataset show that our proposed algorithm can effectively segment the wheat plantation rows images collected by UAV, and is superior to some existing well-known algorithms, and can provide scientific support and reference for the decision-making process of smart agriculture. Threshold convolution Residual-capsule networks Wheat plantation rows Image segmentation Wei, Zhanguo (orcid)0000-0001-9736-502X aut Song, Yaping aut Li, Meilin aut Yang, Xuechun aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2021), 21-23 vom: 24. Juli, Seite 32131-32147 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2021 number:21-23 day:24 month:07 pages:32131-32147 https://doi.org/10.1007/s11042-021-11203-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW SSG-OLC-PHA SSG-OLC-DE-84 AR 80 2021 21-23 24 07 32131-32147 |
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10.1007/s11042-021-11203-5 doi (DE-627)OLC2077131756 (DE-He213)s11042-021-11203-5-p DE-627 ger DE-627 rakwb eng 070 004 VZ Cai, Weiwei verfasserin aut Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold convolution (RCTC) for segmentation of wheat plantation rows. The network is achieved by replacing the AveragePooling of the improved ResNet34 with Capsule. Since the capsule network represents the features by vectors, it can explain the direction of features and the relative positions between features. Therefore, deeper feature information can be extracted. In addition, to reduce redundant features and enhance effective features, a new threshold convolution is also proposed. Experiments on the wheat field dataset show that our proposed algorithm can effectively segment the wheat plantation rows images collected by UAV, and is superior to some existing well-known algorithms, and can provide scientific support and reference for the decision-making process of smart agriculture. Threshold convolution Residual-capsule networks Wheat plantation rows Image segmentation Wei, Zhanguo (orcid)0000-0001-9736-502X aut Song, Yaping aut Li, Meilin aut Yang, Xuechun aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2021), 21-23 vom: 24. Juli, Seite 32131-32147 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2021 number:21-23 day:24 month:07 pages:32131-32147 https://doi.org/10.1007/s11042-021-11203-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW SSG-OLC-PHA SSG-OLC-DE-84 AR 80 2021 21-23 24 07 32131-32147 |
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10.1007/s11042-021-11203-5 doi (DE-627)OLC2077131756 (DE-He213)s11042-021-11203-5-p DE-627 ger DE-627 rakwb eng 070 004 VZ Cai, Weiwei verfasserin aut Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold convolution (RCTC) for segmentation of wheat plantation rows. The network is achieved by replacing the AveragePooling of the improved ResNet34 with Capsule. Since the capsule network represents the features by vectors, it can explain the direction of features and the relative positions between features. Therefore, deeper feature information can be extracted. In addition, to reduce redundant features and enhance effective features, a new threshold convolution is also proposed. Experiments on the wheat field dataset show that our proposed algorithm can effectively segment the wheat plantation rows images collected by UAV, and is superior to some existing well-known algorithms, and can provide scientific support and reference for the decision-making process of smart agriculture. Threshold convolution Residual-capsule networks Wheat plantation rows Image segmentation Wei, Zhanguo (orcid)0000-0001-9736-502X aut Song, Yaping aut Li, Meilin aut Yang, Xuechun aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2021), 21-23 vom: 24. Juli, Seite 32131-32147 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2021 number:21-23 day:24 month:07 pages:32131-32147 https://doi.org/10.1007/s11042-021-11203-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW SSG-OLC-PHA SSG-OLC-DE-84 AR 80 2021 21-23 24 07 32131-32147 |
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residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in uav images |
| title_auth |
Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images |
| abstract |
Abstract The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold convolution (RCTC) for segmentation of wheat plantation rows. The network is achieved by replacing the AveragePooling of the improved ResNet34 with Capsule. Since the capsule network represents the features by vectors, it can explain the direction of features and the relative positions between features. Therefore, deeper feature information can be extracted. In addition, to reduce redundant features and enhance effective features, a new threshold convolution is also proposed. Experiments on the wheat field dataset show that our proposed algorithm can effectively segment the wheat plantation rows images collected by UAV, and is superior to some existing well-known algorithms, and can provide scientific support and reference for the decision-making process of smart agriculture. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold convolution (RCTC) for segmentation of wheat plantation rows. The network is achieved by replacing the AveragePooling of the improved ResNet34 with Capsule. Since the capsule network represents the features by vectors, it can explain the direction of features and the relative positions between features. Therefore, deeper feature information can be extracted. In addition, to reduce redundant features and enhance effective features, a new threshold convolution is also proposed. Experiments on the wheat field dataset show that our proposed algorithm can effectively segment the wheat plantation rows images collected by UAV, and is superior to some existing well-known algorithms, and can provide scientific support and reference for the decision-making process of smart agriculture. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold convolution (RCTC) for segmentation of wheat plantation rows. The network is achieved by replacing the AveragePooling of the improved ResNet34 with Capsule. Since the capsule network represents the features by vectors, it can explain the direction of features and the relative positions between features. Therefore, deeper feature information can be extracted. In addition, to reduce redundant features and enhance effective features, a new threshold convolution is also proposed. Experiments on the wheat field dataset show that our proposed algorithm can effectively segment the wheat plantation rows images collected by UAV, and is superior to some existing well-known algorithms, and can provide scientific support and reference for the decision-making process of smart agriculture. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images |
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https://doi.org/10.1007/s11042-021-11203-5 |
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| author2 |
Wei, Zhanguo Song, Yaping Li, Meilin Yang, Xuechun |
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Wei, Zhanguo Song, Yaping Li, Meilin Yang, Xuechun |
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