Crop pest recognition based on a modified capsule network
Crop pest insects seriously affect yield and quality of crops, and pesticide control methods cause severe environmental pollution, which has inextricably influenced people’s daily lives. Crop pest identification in the field is crucial components of pest control. It is much more complex than generic...
Ausführliche Beschreibung
Autor*in: |
Shanwen Zhang [verfasserIn] Rongzhi Jing [verfasserIn] Xiaoli Shi [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Übergeordnetes Werk: |
In: Systems Science & Control Engineering - Taylor & Francis Group, 2017, 10(2022), 1, Seite 552-561 |
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Übergeordnetes Werk: |
volume:10 ; year:2022 ; number:1 ; pages:552-561 |
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DOI / URN: |
10.1080/21642583.2022.2074168 |
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Katalog-ID: |
DOAJ044089171 |
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10.1080/21642583.2022.2074168 doi (DE-627)DOAJ044089171 (DE-599)DOAJ560c3741522f45e39eff9cdeafe0530f DE-627 ger DE-627 rakwb eng TJ212-225 Shanwen Zhang verfasserin aut Crop pest recognition based on a modified capsule network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop pest insects seriously affect yield and quality of crops, and pesticide control methods cause severe environmental pollution, which has inextricably influenced people’s daily lives. Crop pest identification in the field is crucial components of pest control. It is much more complex than generic object recognition due to the apparent differences in the same pest species in the field with various shapes, colours, sizes and complex background. A crop pest recognition method is proposed based on a modified capsule network (MCapsNet). In MCapsNet, a capsule network is used to improve the traditional convolutional neural network (CNN), and an attention module is introduced to capture the most important classification features and speed up the network training. The experimental results on a pest image dataset validate that the proposed method is effective and feasible in classifying various types of insects in field crops and can be implemented in the agriculture sector for crop protection. Crop pest detection capsule networks (CapsNet) attention mechanism modified CapsNet (MCapsNet) Control engineering systems. Automatic machinery (General) Systems engineering TA168 Rongzhi Jing verfasserin aut Xiaoli Shi verfasserin aut In Systems Science & Control Engineering Taylor & Francis Group, 2017 10(2022), 1, Seite 552-561 (DE-627)737701722 (DE-600)2705530-9 21642583 nnns volume:10 year:2022 number:1 pages:552-561 https://doi.org/10.1080/21642583.2022.2074168 kostenfrei https://doaj.org/article/560c3741522f45e39eff9cdeafe0530f kostenfrei https://www.tandfonline.com/doi/10.1080/21642583.2022.2074168 kostenfrei https://doaj.org/toc/2164-2583 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 1 552-561 |
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10.1080/21642583.2022.2074168 doi (DE-627)DOAJ044089171 (DE-599)DOAJ560c3741522f45e39eff9cdeafe0530f DE-627 ger DE-627 rakwb eng TJ212-225 Shanwen Zhang verfasserin aut Crop pest recognition based on a modified capsule network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop pest insects seriously affect yield and quality of crops, and pesticide control methods cause severe environmental pollution, which has inextricably influenced people’s daily lives. Crop pest identification in the field is crucial components of pest control. It is much more complex than generic object recognition due to the apparent differences in the same pest species in the field with various shapes, colours, sizes and complex background. A crop pest recognition method is proposed based on a modified capsule network (MCapsNet). In MCapsNet, a capsule network is used to improve the traditional convolutional neural network (CNN), and an attention module is introduced to capture the most important classification features and speed up the network training. The experimental results on a pest image dataset validate that the proposed method is effective and feasible in classifying various types of insects in field crops and can be implemented in the agriculture sector for crop protection. Crop pest detection capsule networks (CapsNet) attention mechanism modified CapsNet (MCapsNet) Control engineering systems. Automatic machinery (General) Systems engineering TA168 Rongzhi Jing verfasserin aut Xiaoli Shi verfasserin aut In Systems Science & Control Engineering Taylor & Francis Group, 2017 10(2022), 1, Seite 552-561 (DE-627)737701722 (DE-600)2705530-9 21642583 nnns volume:10 year:2022 number:1 pages:552-561 https://doi.org/10.1080/21642583.2022.2074168 kostenfrei https://doaj.org/article/560c3741522f45e39eff9cdeafe0530f kostenfrei https://www.tandfonline.com/doi/10.1080/21642583.2022.2074168 kostenfrei https://doaj.org/toc/2164-2583 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 1 552-561 |
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10.1080/21642583.2022.2074168 doi (DE-627)DOAJ044089171 (DE-599)DOAJ560c3741522f45e39eff9cdeafe0530f DE-627 ger DE-627 rakwb eng TJ212-225 Shanwen Zhang verfasserin aut Crop pest recognition based on a modified capsule network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop pest insects seriously affect yield and quality of crops, and pesticide control methods cause severe environmental pollution, which has inextricably influenced people’s daily lives. Crop pest identification in the field is crucial components of pest control. It is much more complex than generic object recognition due to the apparent differences in the same pest species in the field with various shapes, colours, sizes and complex background. A crop pest recognition method is proposed based on a modified capsule network (MCapsNet). In MCapsNet, a capsule network is used to improve the traditional convolutional neural network (CNN), and an attention module is introduced to capture the most important classification features and speed up the network training. The experimental results on a pest image dataset validate that the proposed method is effective and feasible in classifying various types of insects in field crops and can be implemented in the agriculture sector for crop protection. Crop pest detection capsule networks (CapsNet) attention mechanism modified CapsNet (MCapsNet) Control engineering systems. Automatic machinery (General) Systems engineering TA168 Rongzhi Jing verfasserin aut Xiaoli Shi verfasserin aut In Systems Science & Control Engineering Taylor & Francis Group, 2017 10(2022), 1, Seite 552-561 (DE-627)737701722 (DE-600)2705530-9 21642583 nnns volume:10 year:2022 number:1 pages:552-561 https://doi.org/10.1080/21642583.2022.2074168 kostenfrei https://doaj.org/article/560c3741522f45e39eff9cdeafe0530f kostenfrei https://www.tandfonline.com/doi/10.1080/21642583.2022.2074168 kostenfrei https://doaj.org/toc/2164-2583 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 1 552-561 |
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10.1080/21642583.2022.2074168 doi (DE-627)DOAJ044089171 (DE-599)DOAJ560c3741522f45e39eff9cdeafe0530f DE-627 ger DE-627 rakwb eng TJ212-225 Shanwen Zhang verfasserin aut Crop pest recognition based on a modified capsule network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop pest insects seriously affect yield and quality of crops, and pesticide control methods cause severe environmental pollution, which has inextricably influenced people’s daily lives. Crop pest identification in the field is crucial components of pest control. It is much more complex than generic object recognition due to the apparent differences in the same pest species in the field with various shapes, colours, sizes and complex background. A crop pest recognition method is proposed based on a modified capsule network (MCapsNet). In MCapsNet, a capsule network is used to improve the traditional convolutional neural network (CNN), and an attention module is introduced to capture the most important classification features and speed up the network training. The experimental results on a pest image dataset validate that the proposed method is effective and feasible in classifying various types of insects in field crops and can be implemented in the agriculture sector for crop protection. Crop pest detection capsule networks (CapsNet) attention mechanism modified CapsNet (MCapsNet) Control engineering systems. Automatic machinery (General) Systems engineering TA168 Rongzhi Jing verfasserin aut Xiaoli Shi verfasserin aut In Systems Science & Control Engineering Taylor & Francis Group, 2017 10(2022), 1, Seite 552-561 (DE-627)737701722 (DE-600)2705530-9 21642583 nnns volume:10 year:2022 number:1 pages:552-561 https://doi.org/10.1080/21642583.2022.2074168 kostenfrei https://doaj.org/article/560c3741522f45e39eff9cdeafe0530f kostenfrei https://www.tandfonline.com/doi/10.1080/21642583.2022.2074168 kostenfrei https://doaj.org/toc/2164-2583 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 1 552-561 |
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Crop pest insects seriously affect yield and quality of crops, and pesticide control methods cause severe environmental pollution, which has inextricably influenced people’s daily lives. Crop pest identification in the field is crucial components of pest control. It is much more complex than generic object recognition due to the apparent differences in the same pest species in the field with various shapes, colours, sizes and complex background. A crop pest recognition method is proposed based on a modified capsule network (MCapsNet). In MCapsNet, a capsule network is used to improve the traditional convolutional neural network (CNN), and an attention module is introduced to capture the most important classification features and speed up the network training. The experimental results on a pest image dataset validate that the proposed method is effective and feasible in classifying various types of insects in field crops and can be implemented in the agriculture sector for crop protection. |
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Crop pest insects seriously affect yield and quality of crops, and pesticide control methods cause severe environmental pollution, which has inextricably influenced people’s daily lives. Crop pest identification in the field is crucial components of pest control. It is much more complex than generic object recognition due to the apparent differences in the same pest species in the field with various shapes, colours, sizes and complex background. A crop pest recognition method is proposed based on a modified capsule network (MCapsNet). In MCapsNet, a capsule network is used to improve the traditional convolutional neural network (CNN), and an attention module is introduced to capture the most important classification features and speed up the network training. The experimental results on a pest image dataset validate that the proposed method is effective and feasible in classifying various types of insects in field crops and can be implemented in the agriculture sector for crop protection. |
abstract_unstemmed |
Crop pest insects seriously affect yield and quality of crops, and pesticide control methods cause severe environmental pollution, which has inextricably influenced people’s daily lives. Crop pest identification in the field is crucial components of pest control. It is much more complex than generic object recognition due to the apparent differences in the same pest species in the field with various shapes, colours, sizes and complex background. A crop pest recognition method is proposed based on a modified capsule network (MCapsNet). In MCapsNet, a capsule network is used to improve the traditional convolutional neural network (CNN), and an attention module is introduced to capture the most important classification features and speed up the network training. The experimental results on a pest image dataset validate that the proposed method is effective and feasible in classifying various types of insects in field crops and can be implemented in the agriculture sector for crop protection. |
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|
score |
7.399374 |