Method of plant leaf recognition based on improved deep convolutional neural network

The identification of plant species mainly depends on the recognition of plant leaf characteristics. However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhu, Xiaolong [verfasserIn] Zhu, Meng [verfasserIn] Ren, Honge [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Deep learning Convolutional neural network Leaf recognition Complex background

Übergeordnetes Werk:	Enthalten in: Cognitive systems research - Amsterdam [u.a.] : Elsevier Science, 1999, 52, Seite 223-233
Übergeordnetes Werk:	volume:52 ; pages:223-233

DOI / URN:	10.1016/j.cogsys.2018.06.008

Katalog-ID:	ELV001109383

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520			\|a The identification of plant species mainly depends on the recognition of plant leaf characteristics. However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in the complex environment, this paper proposes an improved deep convolutional neural network, which takes advantage of the Inception V2 with batch normalization (BN) instead of convolutional neural layers in the faster region convolutional neural network (Faster RCNN) offering multiscale image features to the region proposal network (RPN). In addition, the original images first are cut into the specified size according to the numerical order, and the segmented images are loaded into the proposed network sequentially. After the precise classification through softmax and bounding box regressor, the segmented images with identification labels are spliced together as final output images. The experimental results show that the proposed approach has higher recognition accuracy than Faster RCNN in recognizing leaf species in the complex background.
650		4	\|a Deep learning
650		4	\|a Convolutional neural network
650		4	\|a Leaf recognition
650		4	\|a Complex background
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700	1		\|a Ren, Honge \|e verfasserin \|4 aut
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allfields	10.1016/j.cogsys.2018.06.008 doi (DE-627)ELV001109383 (ELSEVIER)S1389-0417(17)30348-0 DE-627 ger DE-627 rda eng 150 VZ 54.00 bkl Zhu, Xiaolong verfasserin aut Method of plant leaf recognition based on improved deep convolutional neural network 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The identification of plant species mainly depends on the recognition of plant leaf characteristics. However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in the complex environment, this paper proposes an improved deep convolutional neural network, which takes advantage of the Inception V2 with batch normalization (BN) instead of convolutional neural layers in the faster region convolutional neural network (Faster RCNN) offering multiscale image features to the region proposal network (RPN). In addition, the original images first are cut into the specified size according to the numerical order, and the segmented images are loaded into the proposed network sequentially. After the precise classification through softmax and bounding box regressor, the segmented images with identification labels are spliced together as final output images. The experimental results show that the proposed approach has higher recognition accuracy than Faster RCNN in recognizing leaf species in the complex background. Deep learning Convolutional neural network Leaf recognition Complex background Zhu, Meng verfasserin aut Ren, Honge verfasserin aut Enthalten in Cognitive systems research Amsterdam [u.a.] : Elsevier Science, 1999 52, Seite 223-233 Online-Ressource (DE-627)334288940 (DE-600)2056945-2 (DE-576)259272345 1389-0417 nnns volume:52 pages:223-233 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 52 223-233
spelling	10.1016/j.cogsys.2018.06.008 doi (DE-627)ELV001109383 (ELSEVIER)S1389-0417(17)30348-0 DE-627 ger DE-627 rda eng 150 VZ 54.00 bkl Zhu, Xiaolong verfasserin aut Method of plant leaf recognition based on improved deep convolutional neural network 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The identification of plant species mainly depends on the recognition of plant leaf characteristics. However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in the complex environment, this paper proposes an improved deep convolutional neural network, which takes advantage of the Inception V2 with batch normalization (BN) instead of convolutional neural layers in the faster region convolutional neural network (Faster RCNN) offering multiscale image features to the region proposal network (RPN). In addition, the original images first are cut into the specified size according to the numerical order, and the segmented images are loaded into the proposed network sequentially. After the precise classification through softmax and bounding box regressor, the segmented images with identification labels are spliced together as final output images. The experimental results show that the proposed approach has higher recognition accuracy than Faster RCNN in recognizing leaf species in the complex background. Deep learning Convolutional neural network Leaf recognition Complex background Zhu, Meng verfasserin aut Ren, Honge verfasserin aut Enthalten in Cognitive systems research Amsterdam [u.a.] : Elsevier Science, 1999 52, Seite 223-233 Online-Ressource (DE-627)334288940 (DE-600)2056945-2 (DE-576)259272345 1389-0417 nnns volume:52 pages:223-233 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 52 223-233
allfields_unstemmed	10.1016/j.cogsys.2018.06.008 doi (DE-627)ELV001109383 (ELSEVIER)S1389-0417(17)30348-0 DE-627 ger DE-627 rda eng 150 VZ 54.00 bkl Zhu, Xiaolong verfasserin aut Method of plant leaf recognition based on improved deep convolutional neural network 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The identification of plant species mainly depends on the recognition of plant leaf characteristics. However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in the complex environment, this paper proposes an improved deep convolutional neural network, which takes advantage of the Inception V2 with batch normalization (BN) instead of convolutional neural layers in the faster region convolutional neural network (Faster RCNN) offering multiscale image features to the region proposal network (RPN). In addition, the original images first are cut into the specified size according to the numerical order, and the segmented images are loaded into the proposed network sequentially. After the precise classification through softmax and bounding box regressor, the segmented images with identification labels are spliced together as final output images. The experimental results show that the proposed approach has higher recognition accuracy than Faster RCNN in recognizing leaf species in the complex background. Deep learning Convolutional neural network Leaf recognition Complex background Zhu, Meng verfasserin aut Ren, Honge verfasserin aut Enthalten in Cognitive systems research Amsterdam [u.a.] : Elsevier Science, 1999 52, Seite 223-233 Online-Ressource (DE-627)334288940 (DE-600)2056945-2 (DE-576)259272345 1389-0417 nnns volume:52 pages:223-233 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 52 223-233
allfieldsGer	10.1016/j.cogsys.2018.06.008 doi (DE-627)ELV001109383 (ELSEVIER)S1389-0417(17)30348-0 DE-627 ger DE-627 rda eng 150 VZ 54.00 bkl Zhu, Xiaolong verfasserin aut Method of plant leaf recognition based on improved deep convolutional neural network 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The identification of plant species mainly depends on the recognition of plant leaf characteristics. However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in the complex environment, this paper proposes an improved deep convolutional neural network, which takes advantage of the Inception V2 with batch normalization (BN) instead of convolutional neural layers in the faster region convolutional neural network (Faster RCNN) offering multiscale image features to the region proposal network (RPN). In addition, the original images first are cut into the specified size according to the numerical order, and the segmented images are loaded into the proposed network sequentially. After the precise classification through softmax and bounding box regressor, the segmented images with identification labels are spliced together as final output images. The experimental results show that the proposed approach has higher recognition accuracy than Faster RCNN in recognizing leaf species in the complex background. Deep learning Convolutional neural network Leaf recognition Complex background Zhu, Meng verfasserin aut Ren, Honge verfasserin aut Enthalten in Cognitive systems research Amsterdam [u.a.] : Elsevier Science, 1999 52, Seite 223-233 Online-Ressource (DE-627)334288940 (DE-600)2056945-2 (DE-576)259272345 1389-0417 nnns volume:52 pages:223-233 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 52 223-233
allfieldsSound	10.1016/j.cogsys.2018.06.008 doi (DE-627)ELV001109383 (ELSEVIER)S1389-0417(17)30348-0 DE-627 ger DE-627 rda eng 150 VZ 54.00 bkl Zhu, Xiaolong verfasserin aut Method of plant leaf recognition based on improved deep convolutional neural network 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The identification of plant species mainly depends on the recognition of plant leaf characteristics. However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in the complex environment, this paper proposes an improved deep convolutional neural network, which takes advantage of the Inception V2 with batch normalization (BN) instead of convolutional neural layers in the faster region convolutional neural network (Faster RCNN) offering multiscale image features to the region proposal network (RPN). In addition, the original images first are cut into the specified size according to the numerical order, and the segmented images are loaded into the proposed network sequentially. After the precise classification through softmax and bounding box regressor, the segmented images with identification labels are spliced together as final output images. The experimental results show that the proposed approach has higher recognition accuracy than Faster RCNN in recognizing leaf species in the complex background. Deep learning Convolutional neural network Leaf recognition Complex background Zhu, Meng verfasserin aut Ren, Honge verfasserin aut Enthalten in Cognitive systems research Amsterdam [u.a.] : Elsevier Science, 1999 52, Seite 223-233 Online-Ressource (DE-627)334288940 (DE-600)2056945-2 (DE-576)259272345 1389-0417 nnns volume:52 pages:223-233 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 52 223-233
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author	Zhu, Xiaolong
spellingShingle	Zhu, Xiaolong ddc 150 bkl 54.00 misc Deep learning misc Convolutional neural network misc Leaf recognition misc Complex background Method of plant leaf recognition based on improved deep convolutional neural network
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topic_title	150 VZ 54.00 bkl Method of plant leaf recognition based on improved deep convolutional neural network Deep learning Convolutional neural network Leaf recognition Complex background
topic	ddc 150 bkl 54.00 misc Deep learning misc Convolutional neural network misc Leaf recognition misc Complex background
topic_unstemmed	ddc 150 bkl 54.00 misc Deep learning misc Convolutional neural network misc Leaf recognition misc Complex background
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title	Method of plant leaf recognition based on improved deep convolutional neural network
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title_full	Method of plant leaf recognition based on improved deep convolutional neural network
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title_sort	method of plant leaf recognition based on improved deep convolutional neural network
title_auth	Method of plant leaf recognition based on improved deep convolutional neural network
abstract	The identification of plant species mainly depends on the recognition of plant leaf characteristics. However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in the complex environment, this paper proposes an improved deep convolutional neural network, which takes advantage of the Inception V2 with batch normalization (BN) instead of convolutional neural layers in the faster region convolutional neural network (Faster RCNN) offering multiscale image features to the region proposal network (RPN). In addition, the original images first are cut into the specified size according to the numerical order, and the segmented images are loaded into the proposed network sequentially. After the precise classification through softmax and bounding box regressor, the segmented images with identification labels are spliced together as final output images. The experimental results show that the proposed approach has higher recognition accuracy than Faster RCNN in recognizing leaf species in the complex background.
abstractGer	The identification of plant species mainly depends on the recognition of plant leaf characteristics. However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in the complex environment, this paper proposes an improved deep convolutional neural network, which takes advantage of the Inception V2 with batch normalization (BN) instead of convolutional neural layers in the faster region convolutional neural network (Faster RCNN) offering multiscale image features to the region proposal network (RPN). In addition, the original images first are cut into the specified size according to the numerical order, and the segmented images are loaded into the proposed network sequentially. After the precise classification through softmax and bounding box regressor, the segmented images with identification labels are spliced together as final output images. The experimental results show that the proposed approach has higher recognition accuracy than Faster RCNN in recognizing leaf species in the complex background.
abstract_unstemmed	The identification of plant species mainly depends on the recognition of plant leaf characteristics. However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in the complex environment, this paper proposes an improved deep convolutional neural network, which takes advantage of the Inception V2 with batch normalization (BN) instead of convolutional neural layers in the faster region convolutional neural network (Faster RCNN) offering multiscale image features to the region proposal network (RPN). In addition, the original images first are cut into the specified size according to the numerical order, and the segmented images are loaded into the proposed network sequentially. After the precise classification through softmax and bounding box regressor, the segmented images with identification labels are spliced together as final output images. The experimental results show that the proposed approach has higher recognition accuracy than Faster RCNN in recognizing leaf species in the complex background.
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title_short	Method of plant leaf recognition based on improved deep convolutional neural network
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doi_str	10.1016/j.cogsys.2018.06.008
up_date	2024-07-06T20:15:42.196Z
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However, most recognition systems show the weak performance on detecting small objects like plant leaves in the complicated background. In order to improve the recognition ability of plant leaves in the complex environment, this paper proposes an improved deep convolutional neural network, which takes advantage of the Inception V2 with batch normalization (BN) instead of convolutional neural layers in the faster region convolutional neural network (Faster RCNN) offering multiscale image features to the region proposal network (RPN). In addition, the original images first are cut into the specified size according to the numerical order, and the segmented images are loaded into the proposed network sequentially. After the precise classification through softmax and bounding box regressor, the segmented images with identification labels are spliced together as final output images. The experimental results show that the proposed approach has higher recognition accuracy than Faster RCNN in recognizing leaf species in the complex background.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Convolutional neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Leaf recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Complex background</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Meng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ren, Honge</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten 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Method of plant leaf recognition based on improved deep convolutional neural network

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