Identification and localization of grape diseased leaf images captured by UAV based on CNN

In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and V...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Weihan [verfasserIn] Yu, Xiao [verfasserIn] Chen, Cong [verfasserIn] Gong, Qi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Improved VGG-19 Multi-fusion U-net Convolutional Neural Network Disease identification Disease localization

Übergeordnetes Werk:	Enthalten in: Computers and electronics in agriculture - Amsterdam [u.a.] : Elsevier Science, 1985, 214
Übergeordnetes Werk:	volume:214

DOI / URN:	10.1016/j.compag.2023.108277

Katalog-ID:	ELV065461088

Internformat


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245	1	0	\|a Identification and localization of grape diseased leaf images captured by UAV based on CNN
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520			\|a In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and VGG-19 networks. This paper aims to address the difficulty of obtaining target leaves for recognition in complex environments due to the presence of multiple invalid leaves. First, the feature extraction process is performed on the training and validation datasets to reduce the input parameters of the network and increase the training speed. Second, the test data is divided into three datasets: low-light, jitter interference, and two-factor combination. After recovery processing, the Multi-fusion U-net network is fed to locate diseased leaves. Finally, the improved VGG-19 network was used again to locate and identify the disease. Experimental results show that the proposed procedure achieves satisfactory performance in UAV image processing. The average accuracy of segmentation reaches 71.91%, and the identification rate of disease location is increased by 12.33% after segmentation, which provides a strong practical basis for the implementation of unmanned smart ecological farms.
650		4	\|a Improved VGG-19
650		4	\|a Multi-fusion U-net
650		4	\|a Convolutional Neural Network
650		4	\|a Disease identification
650		4	\|a Disease localization
700	1		\|a Yu, Xiao \|e verfasserin \|0 (orcid)0000-0003-3167-9514 \|4 aut
700	1		\|a Chen, Cong \|e verfasserin \|4 aut
700	1		\|a Gong, Qi \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Computers and electronics in agriculture \|d Amsterdam [u.a.] : Elsevier Science, 1985 \|g 214 \|h Online-Ressource \|w (DE-627)320567826 \|w (DE-600)2016151-7 \|w (DE-576)090955684 \|x 1872-7107 \|7 nnns
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publishDate	2023
allfields	10.1016/j.compag.2023.108277 doi (DE-627)ELV065461088 (ELSEVIER)S0168-1699(23)00665-8 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Li, Weihan verfasserin aut Identification and localization of grape diseased leaf images captured by UAV based on CNN 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and VGG-19 networks. This paper aims to address the difficulty of obtaining target leaves for recognition in complex environments due to the presence of multiple invalid leaves. First, the feature extraction process is performed on the training and validation datasets to reduce the input parameters of the network and increase the training speed. Second, the test data is divided into three datasets: low-light, jitter interference, and two-factor combination. After recovery processing, the Multi-fusion U-net network is fed to locate diseased leaves. Finally, the improved VGG-19 network was used again to locate and identify the disease. Experimental results show that the proposed procedure achieves satisfactory performance in UAV image processing. The average accuracy of segmentation reaches 71.91%, and the identification rate of disease location is increased by 12.33% after segmentation, which provides a strong practical basis for the implementation of unmanned smart ecological farms. Improved VGG-19 Multi-fusion U-net Convolutional Neural Network Disease identification Disease localization Yu, Xiao verfasserin (orcid)0000-0003-3167-9514 aut Chen, Cong verfasserin aut Gong, Qi verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 214 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:214 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 214
spelling	10.1016/j.compag.2023.108277 doi (DE-627)ELV065461088 (ELSEVIER)S0168-1699(23)00665-8 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Li, Weihan verfasserin aut Identification and localization of grape diseased leaf images captured by UAV based on CNN 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and VGG-19 networks. This paper aims to address the difficulty of obtaining target leaves for recognition in complex environments due to the presence of multiple invalid leaves. First, the feature extraction process is performed on the training and validation datasets to reduce the input parameters of the network and increase the training speed. Second, the test data is divided into three datasets: low-light, jitter interference, and two-factor combination. After recovery processing, the Multi-fusion U-net network is fed to locate diseased leaves. Finally, the improved VGG-19 network was used again to locate and identify the disease. Experimental results show that the proposed procedure achieves satisfactory performance in UAV image processing. The average accuracy of segmentation reaches 71.91%, and the identification rate of disease location is increased by 12.33% after segmentation, which provides a strong practical basis for the implementation of unmanned smart ecological farms. Improved VGG-19 Multi-fusion U-net Convolutional Neural Network Disease identification Disease localization Yu, Xiao verfasserin (orcid)0000-0003-3167-9514 aut Chen, Cong verfasserin aut Gong, Qi verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 214 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:214 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 214
allfields_unstemmed	10.1016/j.compag.2023.108277 doi (DE-627)ELV065461088 (ELSEVIER)S0168-1699(23)00665-8 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Li, Weihan verfasserin aut Identification and localization of grape diseased leaf images captured by UAV based on CNN 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and VGG-19 networks. This paper aims to address the difficulty of obtaining target leaves for recognition in complex environments due to the presence of multiple invalid leaves. First, the feature extraction process is performed on the training and validation datasets to reduce the input parameters of the network and increase the training speed. Second, the test data is divided into three datasets: low-light, jitter interference, and two-factor combination. After recovery processing, the Multi-fusion U-net network is fed to locate diseased leaves. Finally, the improved VGG-19 network was used again to locate and identify the disease. Experimental results show that the proposed procedure achieves satisfactory performance in UAV image processing. The average accuracy of segmentation reaches 71.91%, and the identification rate of disease location is increased by 12.33% after segmentation, which provides a strong practical basis for the implementation of unmanned smart ecological farms. Improved VGG-19 Multi-fusion U-net Convolutional Neural Network Disease identification Disease localization Yu, Xiao verfasserin (orcid)0000-0003-3167-9514 aut Chen, Cong verfasserin aut Gong, Qi verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 214 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:214 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 214
allfieldsGer	10.1016/j.compag.2023.108277 doi (DE-627)ELV065461088 (ELSEVIER)S0168-1699(23)00665-8 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Li, Weihan verfasserin aut Identification and localization of grape diseased leaf images captured by UAV based on CNN 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and VGG-19 networks. This paper aims to address the difficulty of obtaining target leaves for recognition in complex environments due to the presence of multiple invalid leaves. First, the feature extraction process is performed on the training and validation datasets to reduce the input parameters of the network and increase the training speed. Second, the test data is divided into three datasets: low-light, jitter interference, and two-factor combination. After recovery processing, the Multi-fusion U-net network is fed to locate diseased leaves. Finally, the improved VGG-19 network was used again to locate and identify the disease. Experimental results show that the proposed procedure achieves satisfactory performance in UAV image processing. The average accuracy of segmentation reaches 71.91%, and the identification rate of disease location is increased by 12.33% after segmentation, which provides a strong practical basis for the implementation of unmanned smart ecological farms. Improved VGG-19 Multi-fusion U-net Convolutional Neural Network Disease identification Disease localization Yu, Xiao verfasserin (orcid)0000-0003-3167-9514 aut Chen, Cong verfasserin aut Gong, Qi verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 214 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:214 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 214
allfieldsSound	10.1016/j.compag.2023.108277 doi (DE-627)ELV065461088 (ELSEVIER)S0168-1699(23)00665-8 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Li, Weihan verfasserin aut Identification and localization of grape diseased leaf images captured by UAV based on CNN 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and VGG-19 networks. This paper aims to address the difficulty of obtaining target leaves for recognition in complex environments due to the presence of multiple invalid leaves. First, the feature extraction process is performed on the training and validation datasets to reduce the input parameters of the network and increase the training speed. Second, the test data is divided into three datasets: low-light, jitter interference, and two-factor combination. After recovery processing, the Multi-fusion U-net network is fed to locate diseased leaves. Finally, the improved VGG-19 network was used again to locate and identify the disease. Experimental results show that the proposed procedure achieves satisfactory performance in UAV image processing. The average accuracy of segmentation reaches 71.91%, and the identification rate of disease location is increased by 12.33% after segmentation, which provides a strong practical basis for the implementation of unmanned smart ecological farms. Improved VGG-19 Multi-fusion U-net Convolutional Neural Network Disease identification Disease localization Yu, Xiao verfasserin (orcid)0000-0003-3167-9514 aut Chen, Cong verfasserin aut Gong, Qi verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 214 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:214 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 214
language	English
source	Enthalten in Computers and electronics in agriculture 214 volume:214
sourceStr	Enthalten in Computers and electronics in agriculture 214 volume:214
format_phy_str_mv	Article
bklname	Methoden und Techniken der Land- und Forstwirtschaft
institution	findex.gbv.de
topic_facet	Improved VGG-19 Multi-fusion U-net Convolutional Neural Network Disease identification Disease localization
dewey-raw	620
isfreeaccess_bool	false
container_title	Computers and electronics in agriculture
authorswithroles_txt_mv	Li, Weihan @@aut@@ Yu, Xiao @@aut@@ Chen, Cong @@aut@@ Gong, Qi @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320567826
dewey-sort	3620
id	ELV065461088
language_de	englisch
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author	Li, Weihan
spellingShingle	Li, Weihan ddc 620 bkl 48.03 misc Improved VGG-19 misc Multi-fusion U-net misc Convolutional Neural Network misc Disease identification misc Disease localization Identification and localization of grape diseased leaf images captured by UAV based on CNN
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topic_title	620 630 640 004 VZ 48.03 bkl Identification and localization of grape diseased leaf images captured by UAV based on CNN Improved VGG-19 Multi-fusion U-net Convolutional Neural Network Disease identification Disease localization
topic	ddc 620 bkl 48.03 misc Improved VGG-19 misc Multi-fusion U-net misc Convolutional Neural Network misc Disease identification misc Disease localization
topic_unstemmed	ddc 620 bkl 48.03 misc Improved VGG-19 misc Multi-fusion U-net misc Convolutional Neural Network misc Disease identification misc Disease localization
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title	Identification and localization of grape diseased leaf images captured by UAV based on CNN
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title_full	Identification and localization of grape diseased leaf images captured by UAV based on CNN
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title_sort	identification and localization of grape diseased leaf images captured by uav based on cnn
title_auth	Identification and localization of grape diseased leaf images captured by UAV based on CNN
abstract	In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and VGG-19 networks. This paper aims to address the difficulty of obtaining target leaves for recognition in complex environments due to the presence of multiple invalid leaves. First, the feature extraction process is performed on the training and validation datasets to reduce the input parameters of the network and increase the training speed. Second, the test data is divided into three datasets: low-light, jitter interference, and two-factor combination. After recovery processing, the Multi-fusion U-net network is fed to locate diseased leaves. Finally, the improved VGG-19 network was used again to locate and identify the disease. Experimental results show that the proposed procedure achieves satisfactory performance in UAV image processing. The average accuracy of segmentation reaches 71.91%, and the identification rate of disease location is increased by 12.33% after segmentation, which provides a strong practical basis for the implementation of unmanned smart ecological farms.
abstractGer	In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and VGG-19 networks. This paper aims to address the difficulty of obtaining target leaves for recognition in complex environments due to the presence of multiple invalid leaves. First, the feature extraction process is performed on the training and validation datasets to reduce the input parameters of the network and increase the training speed. Second, the test data is divided into three datasets: low-light, jitter interference, and two-factor combination. After recovery processing, the Multi-fusion U-net network is fed to locate diseased leaves. Finally, the improved VGG-19 network was used again to locate and identify the disease. Experimental results show that the proposed procedure achieves satisfactory performance in UAV image processing. The average accuracy of segmentation reaches 71.91%, and the identification rate of disease location is increased by 12.33% after segmentation, which provides a strong practical basis for the implementation of unmanned smart ecological farms.
abstract_unstemmed	In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and VGG-19 networks. This paper aims to address the difficulty of obtaining target leaves for recognition in complex environments due to the presence of multiple invalid leaves. First, the feature extraction process is performed on the training and validation datasets to reduce the input parameters of the network and increase the training speed. Second, the test data is divided into three datasets: low-light, jitter interference, and two-factor combination. After recovery processing, the Multi-fusion U-net network is fed to locate diseased leaves. Finally, the improved VGG-19 network was used again to locate and identify the disease. Experimental results show that the proposed procedure achieves satisfactory performance in UAV image processing. The average accuracy of segmentation reaches 71.91%, and the identification rate of disease location is increased by 12.33% after segmentation, which provides a strong practical basis for the implementation of unmanned smart ecological farms.
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title_short	Identification and localization of grape diseased leaf images captured by UAV based on CNN
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author2	Yu, Xiao Chen, Cong Gong, Qi
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doi_str	10.1016/j.compag.2023.108277
up_date	2024-07-06T23:06:41.579Z
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Identification and localization of grape diseased leaf images captured by UAV based on CNN

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