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

Gespeichert in:

Autor*in:	Shanwen Zhang [verfasserIn] Rongzhi Jing [verfasserIn] Xiaoli Shi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Crop pest detection capsule networks (CapsNet) attention mechanism modified CapsNet (MCapsNet)

Übergeordnetes Werk:	In: Systems Science & Control Engineering - Taylor & Francis Group, 2017, 10(2022), 1, Seite 552-561
Übergeordnetes Werk:	volume:10 ; year:2022 ; number:1 ; pages:552-561

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1080/21642583.2022.2074168

Katalog-ID:	DOAJ044089171

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ044089171
003	DE-627
005	20230308075817.0
007	cr uuu---uuuuu
008	230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1080/21642583.2022.2074168 \|2 doi
035			\|a (DE-627)DOAJ044089171
035			\|a (DE-599)DOAJ560c3741522f45e39eff9cdeafe0530f
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TJ212-225
100	0		\|a Shanwen Zhang \|e verfasserin \|4 aut
245	1	0	\|a Crop pest recognition based on a modified capsule network
264		1	\|c 2022
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a 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.
650		4	\|a Crop pest detection
650		4	\|a capsule networks (CapsNet)
650		4	\|a attention mechanism
650		4	\|a modified CapsNet (MCapsNet)
653		0	\|a Control engineering systems. Automatic machinery (General)
653		0	\|a Systems engineering
653		0	\|a TA168
700	0		\|a Rongzhi Jing \|e verfasserin \|4 aut
700	0		\|a Xiaoli Shi \|e verfasserin \|4 aut
773	0	8	\|i In \|t Systems Science & Control Engineering \|d Taylor & Francis Group, 2017 \|g 10(2022), 1, Seite 552-561 \|w (DE-627)737701722 \|w (DE-600)2705530-9 \|x 21642583 \|7 nnns
773	1	8	\|g volume:10 \|g year:2022 \|g number:1 \|g pages:552-561
856	4	0	\|u https://doi.org/10.1080/21642583.2022.2074168 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/560c3741522f45e39eff9cdeafe0530f \|z kostenfrei
856	4	0	\|u https://www.tandfonline.com/doi/10.1080/21642583.2022.2074168 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2164-2583 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 10 \|j 2022 \|e 1 \|h 552-561

Indexfelder

author_variant	s z sz r j rj x s xs
matchkey_str	article:21642583:2022----::rpeteontobsdnmdfec
hierarchy_sort_str	2022
callnumber-subject-code	TJ
publishDate	2022
allfields	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
spelling	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
allfields_unstemmed	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
allfieldsGer	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
allfieldsSound	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
language	English
source	In Systems Science & Control Engineering 10(2022), 1, Seite 552-561 volume:10 year:2022 number:1 pages:552-561
sourceStr	In Systems Science & Control Engineering 10(2022), 1, Seite 552-561 volume:10 year:2022 number:1 pages:552-561
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Crop pest detection capsule networks (CapsNet) attention mechanism modified CapsNet (MCapsNet) Control engineering systems. Automatic machinery (General) Systems engineering TA168
isfreeaccess_bool	true
container_title	Systems Science & Control Engineering
authorswithroles_txt_mv	Shanwen Zhang @@aut@@ Rongzhi Jing @@aut@@ Xiaoli Shi @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	737701722
id	DOAJ044089171
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ044089171</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308075817.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/21642583.2022.2074168</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ044089171</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ560c3741522f45e39eff9cdeafe0530f</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TJ212-225</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Shanwen Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Crop pest recognition based on a modified capsule network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Crop pest detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">capsule networks (CapsNet)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">attention mechanism</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">modified CapsNet (MCapsNet)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Control engineering systems. Automatic machinery (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Systems engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">TA168</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rongzhi Jing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiaoli Shi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Systems Science & Control Engineering</subfield><subfield code="d">Taylor & Francis Group, 2017</subfield><subfield code="g">10(2022), 1, Seite 552-561</subfield><subfield code="w">(DE-627)737701722</subfield><subfield code="w">(DE-600)2705530-9</subfield><subfield code="x">21642583</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:552-561</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1080/21642583.2022.2074168</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/560c3741522f45e39eff9cdeafe0530f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.tandfonline.com/doi/10.1080/21642583.2022.2074168</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2164-2583</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2022</subfield><subfield code="e">1</subfield><subfield code="h">552-561</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Shanwen Zhang
spellingShingle	Shanwen Zhang misc TJ212-225 misc Crop pest detection misc capsule networks (CapsNet) misc attention mechanism misc modified CapsNet (MCapsNet) misc Control engineering systems. Automatic machinery (General) misc Systems engineering misc TA168 Crop pest recognition based on a modified capsule network
authorStr	Shanwen Zhang
ppnlink_with_tag_str_mv	@@773@@(DE-627)737701722
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TJ212-225
illustrated	Not Illustrated
issn	21642583
topic_title	TJ212-225 Crop pest recognition based on a modified capsule network Crop pest detection capsule networks (CapsNet) attention mechanism modified CapsNet (MCapsNet)
topic	misc TJ212-225 misc Crop pest detection misc capsule networks (CapsNet) misc attention mechanism misc modified CapsNet (MCapsNet) misc Control engineering systems. Automatic machinery (General) misc Systems engineering misc TA168
topic_unstemmed	misc TJ212-225 misc Crop pest detection misc capsule networks (CapsNet) misc attention mechanism misc modified CapsNet (MCapsNet) misc Control engineering systems. Automatic machinery (General) misc Systems engineering misc TA168
topic_browse	misc TJ212-225 misc Crop pest detection misc capsule networks (CapsNet) misc attention mechanism misc modified CapsNet (MCapsNet) misc Control engineering systems. Automatic machinery (General) misc Systems engineering misc TA168
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Systems Science & Control Engineering
hierarchy_parent_id	737701722
hierarchy_top_title	Systems Science & Control Engineering
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)737701722 (DE-600)2705530-9
title	Crop pest recognition based on a modified capsule network
ctrlnum	(DE-627)DOAJ044089171 (DE-599)DOAJ560c3741522f45e39eff9cdeafe0530f
title_full	Crop pest recognition based on a modified capsule network
author_sort	Shanwen Zhang
journal	Systems Science & Control Engineering
journalStr	Systems Science & Control Engineering
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2022
contenttype_str_mv	txt
container_start_page	552
author_browse	Shanwen Zhang Rongzhi Jing Xiaoli Shi
container_volume	10
class	TJ212-225
format_se	Elektronische Aufsätze
author-letter	Shanwen Zhang
doi_str_mv	10.1080/21642583.2022.2074168
author2-role	verfasserin
title_sort	crop pest recognition based on a modified capsule network
callnumber	TJ212-225
title_auth	Crop pest recognition based on a modified capsule network
abstract	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.
abstractGer	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.
collection_details	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
container_issue	1
title_short	Crop pest recognition based on a modified capsule network
url	https://doi.org/10.1080/21642583.2022.2074168 https://doaj.org/article/560c3741522f45e39eff9cdeafe0530f https://www.tandfonline.com/doi/10.1080/21642583.2022.2074168 https://doaj.org/toc/2164-2583
remote_bool	true
author2	Rongzhi Jing Xiaoli Shi
author2Str	Rongzhi Jing Xiaoli Shi
ppnlink	737701722
callnumber-subject	TJ - Mechanical Engineering and Machinery
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1080/21642583.2022.2074168
callnumber-a	TJ212-225
up_date	2024-07-03T21:09:41.691Z
_version_	1803593692418670592
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ044089171</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308075817.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/21642583.2022.2074168</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ044089171</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ560c3741522f45e39eff9cdeafe0530f</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TJ212-225</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Shanwen Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Crop pest recognition based on a modified capsule network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Crop pest detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">capsule networks (CapsNet)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">attention mechanism</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">modified CapsNet (MCapsNet)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Control engineering systems. Automatic machinery (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Systems engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">TA168</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rongzhi Jing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiaoli Shi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Systems Science & Control Engineering</subfield><subfield code="d">Taylor & Francis Group, 2017</subfield><subfield code="g">10(2022), 1, Seite 552-561</subfield><subfield code="w">(DE-627)737701722</subfield><subfield code="w">(DE-600)2705530-9</subfield><subfield code="x">21642583</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:552-561</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1080/21642583.2022.2074168</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/560c3741522f45e39eff9cdeafe0530f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.tandfonline.com/doi/10.1080/21642583.2022.2074168</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2164-2583</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2022</subfield><subfield code="e">1</subfield><subfield code="h">552-561</subfield></datafield></record></collection>
score	7.399374

Nicht das Richtige dabei?

Schreiben Sie uns!

Crop pest recognition based on a modified capsule network

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?