Maturity identification and category determination method of broccoli based on semantic segmentation models

The critical technology of the broccoli selective harvesting robot centres around the maturity identification and determination of broccoli heads suitable for harvesting. To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kang, Shuo [verfasserIn] Li, Dongfang [verfasserIn] Li, Boliao [verfasserIn] Zhu, Jianxi [verfasserIn] Long, Sifang [verfasserIn] Wang, Jun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Maturity identification Semantic segmentation Selective harvesting Category determination method Broccoli

Übergeordnetes Werk:	Enthalten in: Computers and electronics in agriculture - Amsterdam [u.a.] : Elsevier Science, 1985, 217
Übergeordnetes Werk:	volume:217

DOI / URN:	10.1016/j.compag.2024.108633

Katalog-ID:	ELV066871123

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520			\|a The critical technology of the broccoli selective harvesting robot centres around the maturity identification and determination of broccoli heads suitable for harvesting. To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research. This method enables broccoli head detection, pixel-level identification, determination of maturity categories, and precise localisation of suitable heads for harvesting, thus better aligning with practical harvesting scenarios. The maturity identification method is based on the DeepLabV3+ network model, which classifies pixel points into four categories: immature, semi-mature, mature, and hypermature. Furthermore, targeted enhancements to the network structure have been incorporated to accommodate the unique maturity characteristics of broccoli. MobileNetV2 contributes to the real-time detection of multiple broccoli heads within the view of camera. The Dense Atrous Spatial Pyramid Pooling (DASPP) module enhances the capability of recognising multiscale features of broccoli, and the Convolutional Block Attention Module (CBAM) further improves the integration of maturity information. The effectiveness of the targeted enhancements has been validated through ablation experiments. The semantic segmentation was successfully applied to broccoli maturity identification for the first time by incorporating a self-designed category determination module. The proposed algorithm achieves a mean intersection over union (mIoU) exceeding 57.9 %, the pixel accuracy (PA) reaching 98.56 %, and the mean category prediction accuracy (mCPA) of 70.93 %. These performance metrics outperform established algorithms such as BASNet, DeepLabV3+, and UNet. This advancement has resulted in an enhancement in the accuracy of maturity identification and a substantial reduction in computational expenses.
650		4	\|a Maturity identification
650		4	\|a Semantic segmentation
650		4	\|a Selective harvesting
650		4	\|a Category determination method
650		4	\|a Broccoli
700	1		\|a Li, Dongfang \|e verfasserin \|4 aut
700	1		\|a Li, Boliao \|e verfasserin \|4 aut
700	1		\|a Zhu, Jianxi \|e verfasserin \|4 aut
700	1		\|a Long, Sifang \|e verfasserin \|4 aut
700	1		\|a Wang, Jun \|e verfasserin \|0 (orcid)0000-0001-5767-6149 \|4 aut
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publishDate	2024
allfields	10.1016/j.compag.2024.108633 doi (DE-627)ELV066871123 (ELSEVIER)S0168-1699(24)00024-3 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Kang, Shuo verfasserin (orcid)0000-0002-4492-3929 aut Maturity identification and category determination method of broccoli based on semantic segmentation models 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The critical technology of the broccoli selective harvesting robot centres around the maturity identification and determination of broccoli heads suitable for harvesting. To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research. This method enables broccoli head detection, pixel-level identification, determination of maturity categories, and precise localisation of suitable heads for harvesting, thus better aligning with practical harvesting scenarios. The maturity identification method is based on the DeepLabV3+ network model, which classifies pixel points into four categories: immature, semi-mature, mature, and hypermature. Furthermore, targeted enhancements to the network structure have been incorporated to accommodate the unique maturity characteristics of broccoli. MobileNetV2 contributes to the real-time detection of multiple broccoli heads within the view of camera. The Dense Atrous Spatial Pyramid Pooling (DASPP) module enhances the capability of recognising multiscale features of broccoli, and the Convolutional Block Attention Module (CBAM) further improves the integration of maturity information. The effectiveness of the targeted enhancements has been validated through ablation experiments. The semantic segmentation was successfully applied to broccoli maturity identification for the first time by incorporating a self-designed category determination module. The proposed algorithm achieves a mean intersection over union (mIoU) exceeding 57.9 %, the pixel accuracy (PA) reaching 98.56 %, and the mean category prediction accuracy (mCPA) of 70.93 %. These performance metrics outperform established algorithms such as BASNet, DeepLabV3+, and UNet. This advancement has resulted in an enhancement in the accuracy of maturity identification and a substantial reduction in computational expenses. Maturity identification Semantic segmentation Selective harvesting Category determination method Broccoli Li, Dongfang verfasserin aut Li, Boliao verfasserin aut Zhu, Jianxi verfasserin aut Long, Sifang verfasserin aut Wang, Jun verfasserin (orcid)0000-0001-5767-6149 aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 217 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:217 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 217
spelling	10.1016/j.compag.2024.108633 doi (DE-627)ELV066871123 (ELSEVIER)S0168-1699(24)00024-3 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Kang, Shuo verfasserin (orcid)0000-0002-4492-3929 aut Maturity identification and category determination method of broccoli based on semantic segmentation models 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The critical technology of the broccoli selective harvesting robot centres around the maturity identification and determination of broccoli heads suitable for harvesting. To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research. This method enables broccoli head detection, pixel-level identification, determination of maturity categories, and precise localisation of suitable heads for harvesting, thus better aligning with practical harvesting scenarios. The maturity identification method is based on the DeepLabV3+ network model, which classifies pixel points into four categories: immature, semi-mature, mature, and hypermature. Furthermore, targeted enhancements to the network structure have been incorporated to accommodate the unique maturity characteristics of broccoli. MobileNetV2 contributes to the real-time detection of multiple broccoli heads within the view of camera. The Dense Atrous Spatial Pyramid Pooling (DASPP) module enhances the capability of recognising multiscale features of broccoli, and the Convolutional Block Attention Module (CBAM) further improves the integration of maturity information. The effectiveness of the targeted enhancements has been validated through ablation experiments. The semantic segmentation was successfully applied to broccoli maturity identification for the first time by incorporating a self-designed category determination module. The proposed algorithm achieves a mean intersection over union (mIoU) exceeding 57.9 %, the pixel accuracy (PA) reaching 98.56 %, and the mean category prediction accuracy (mCPA) of 70.93 %. These performance metrics outperform established algorithms such as BASNet, DeepLabV3+, and UNet. This advancement has resulted in an enhancement in the accuracy of maturity identification and a substantial reduction in computational expenses. Maturity identification Semantic segmentation Selective harvesting Category determination method Broccoli Li, Dongfang verfasserin aut Li, Boliao verfasserin aut Zhu, Jianxi verfasserin aut Long, Sifang verfasserin aut Wang, Jun verfasserin (orcid)0000-0001-5767-6149 aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 217 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:217 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 217
allfields_unstemmed	10.1016/j.compag.2024.108633 doi (DE-627)ELV066871123 (ELSEVIER)S0168-1699(24)00024-3 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Kang, Shuo verfasserin (orcid)0000-0002-4492-3929 aut Maturity identification and category determination method of broccoli based on semantic segmentation models 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The critical technology of the broccoli selective harvesting robot centres around the maturity identification and determination of broccoli heads suitable for harvesting. To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research. This method enables broccoli head detection, pixel-level identification, determination of maturity categories, and precise localisation of suitable heads for harvesting, thus better aligning with practical harvesting scenarios. The maturity identification method is based on the DeepLabV3+ network model, which classifies pixel points into four categories: immature, semi-mature, mature, and hypermature. Furthermore, targeted enhancements to the network structure have been incorporated to accommodate the unique maturity characteristics of broccoli. MobileNetV2 contributes to the real-time detection of multiple broccoli heads within the view of camera. The Dense Atrous Spatial Pyramid Pooling (DASPP) module enhances the capability of recognising multiscale features of broccoli, and the Convolutional Block Attention Module (CBAM) further improves the integration of maturity information. The effectiveness of the targeted enhancements has been validated through ablation experiments. The semantic segmentation was successfully applied to broccoli maturity identification for the first time by incorporating a self-designed category determination module. The proposed algorithm achieves a mean intersection over union (mIoU) exceeding 57.9 %, the pixel accuracy (PA) reaching 98.56 %, and the mean category prediction accuracy (mCPA) of 70.93 %. These performance metrics outperform established algorithms such as BASNet, DeepLabV3+, and UNet. This advancement has resulted in an enhancement in the accuracy of maturity identification and a substantial reduction in computational expenses. Maturity identification Semantic segmentation Selective harvesting Category determination method Broccoli Li, Dongfang verfasserin aut Li, Boliao verfasserin aut Zhu, Jianxi verfasserin aut Long, Sifang verfasserin aut Wang, Jun verfasserin (orcid)0000-0001-5767-6149 aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 217 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:217 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 217
allfieldsGer	10.1016/j.compag.2024.108633 doi (DE-627)ELV066871123 (ELSEVIER)S0168-1699(24)00024-3 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Kang, Shuo verfasserin (orcid)0000-0002-4492-3929 aut Maturity identification and category determination method of broccoli based on semantic segmentation models 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The critical technology of the broccoli selective harvesting robot centres around the maturity identification and determination of broccoli heads suitable for harvesting. To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research. This method enables broccoli head detection, pixel-level identification, determination of maturity categories, and precise localisation of suitable heads for harvesting, thus better aligning with practical harvesting scenarios. The maturity identification method is based on the DeepLabV3+ network model, which classifies pixel points into four categories: immature, semi-mature, mature, and hypermature. Furthermore, targeted enhancements to the network structure have been incorporated to accommodate the unique maturity characteristics of broccoli. MobileNetV2 contributes to the real-time detection of multiple broccoli heads within the view of camera. The Dense Atrous Spatial Pyramid Pooling (DASPP) module enhances the capability of recognising multiscale features of broccoli, and the Convolutional Block Attention Module (CBAM) further improves the integration of maturity information. The effectiveness of the targeted enhancements has been validated through ablation experiments. The semantic segmentation was successfully applied to broccoli maturity identification for the first time by incorporating a self-designed category determination module. The proposed algorithm achieves a mean intersection over union (mIoU) exceeding 57.9 %, the pixel accuracy (PA) reaching 98.56 %, and the mean category prediction accuracy (mCPA) of 70.93 %. These performance metrics outperform established algorithms such as BASNet, DeepLabV3+, and UNet. This advancement has resulted in an enhancement in the accuracy of maturity identification and a substantial reduction in computational expenses. Maturity identification Semantic segmentation Selective harvesting Category determination method Broccoli Li, Dongfang verfasserin aut Li, Boliao verfasserin aut Zhu, Jianxi verfasserin aut Long, Sifang verfasserin aut Wang, Jun verfasserin (orcid)0000-0001-5767-6149 aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 217 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:217 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 217
allfieldsSound	10.1016/j.compag.2024.108633 doi (DE-627)ELV066871123 (ELSEVIER)S0168-1699(24)00024-3 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Kang, Shuo verfasserin (orcid)0000-0002-4492-3929 aut Maturity identification and category determination method of broccoli based on semantic segmentation models 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The critical technology of the broccoli selective harvesting robot centres around the maturity identification and determination of broccoli heads suitable for harvesting. To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research. This method enables broccoli head detection, pixel-level identification, determination of maturity categories, and precise localisation of suitable heads for harvesting, thus better aligning with practical harvesting scenarios. The maturity identification method is based on the DeepLabV3+ network model, which classifies pixel points into four categories: immature, semi-mature, mature, and hypermature. Furthermore, targeted enhancements to the network structure have been incorporated to accommodate the unique maturity characteristics of broccoli. MobileNetV2 contributes to the real-time detection of multiple broccoli heads within the view of camera. The Dense Atrous Spatial Pyramid Pooling (DASPP) module enhances the capability of recognising multiscale features of broccoli, and the Convolutional Block Attention Module (CBAM) further improves the integration of maturity information. The effectiveness of the targeted enhancements has been validated through ablation experiments. The semantic segmentation was successfully applied to broccoli maturity identification for the first time by incorporating a self-designed category determination module. The proposed algorithm achieves a mean intersection over union (mIoU) exceeding 57.9 %, the pixel accuracy (PA) reaching 98.56 %, and the mean category prediction accuracy (mCPA) of 70.93 %. These performance metrics outperform established algorithms such as BASNet, DeepLabV3+, and UNet. This advancement has resulted in an enhancement in the accuracy of maturity identification and a substantial reduction in computational expenses. Maturity identification Semantic segmentation Selective harvesting Category determination method Broccoli Li, Dongfang verfasserin aut Li, Boliao verfasserin aut Zhu, Jianxi verfasserin aut Long, Sifang verfasserin aut Wang, Jun verfasserin (orcid)0000-0001-5767-6149 aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 217 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:217 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 217
language	English
source	Enthalten in Computers and electronics in agriculture 217 volume:217
sourceStr	Enthalten in Computers and electronics in agriculture 217 volume:217
format_phy_str_mv	Article
bklname	Methoden und Techniken der Land- und Forstwirtschaft
institution	findex.gbv.de
topic_facet	Maturity identification Semantic segmentation Selective harvesting Category determination method Broccoli
dewey-raw	620
isfreeaccess_bool	false
container_title	Computers and electronics in agriculture
authorswithroles_txt_mv	Kang, Shuo @@aut@@ Li, Dongfang @@aut@@ Li, Boliao @@aut@@ Zhu, Jianxi @@aut@@ Long, Sifang @@aut@@ Wang, Jun @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	320567826
dewey-sort	3620
id	ELV066871123
language_de	englisch
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To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research. This method enables broccoli head detection, pixel-level identification, determination of maturity categories, and precise localisation of suitable heads for harvesting, thus better aligning with practical harvesting scenarios. The maturity identification method is based on the DeepLabV3+ network model, which classifies pixel points into four categories: immature, semi-mature, mature, and hypermature. Furthermore, targeted enhancements to the network structure have been incorporated to accommodate the unique maturity characteristics of broccoli. MobileNetV2 contributes to the real-time detection of multiple broccoli heads within the view of camera. The Dense Atrous Spatial Pyramid Pooling (DASPP) module enhances the capability of recognising multiscale features of broccoli, and the Convolutional Block Attention Module (CBAM) further improves the integration of maturity information. The effectiveness of the targeted enhancements has been validated through ablation experiments. The semantic segmentation was successfully applied to broccoli maturity identification for the first time by incorporating a self-designed category determination module. The proposed algorithm achieves a mean intersection over union (mIoU) exceeding 57.9 %, the pixel accuracy (PA) reaching 98.56 %, and the mean category prediction accuracy (mCPA) of 70.93 %. These performance metrics outperform established algorithms such as BASNet, DeepLabV3+, and UNet. 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author	Kang, Shuo
spellingShingle	Kang, Shuo ddc 620 bkl 48.03 misc Maturity identification misc Semantic segmentation misc Selective harvesting misc Category determination method misc Broccoli Maturity identification and category determination method of broccoli based on semantic segmentation models
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topic	ddc 620 bkl 48.03 misc Maturity identification misc Semantic segmentation misc Selective harvesting misc Category determination method misc Broccoli
topic_unstemmed	ddc 620 bkl 48.03 misc Maturity identification misc Semantic segmentation misc Selective harvesting misc Category determination method misc Broccoli
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title	Maturity identification and category determination method of broccoli based on semantic segmentation models
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title_full	Maturity identification and category determination method of broccoli based on semantic segmentation models
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title_sort	maturity identification and category determination method of broccoli based on semantic segmentation models
title_auth	Maturity identification and category determination method of broccoli based on semantic segmentation models
abstract	The critical technology of the broccoli selective harvesting robot centres around the maturity identification and determination of broccoli heads suitable for harvesting. To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research. This method enables broccoli head detection, pixel-level identification, determination of maturity categories, and precise localisation of suitable heads for harvesting, thus better aligning with practical harvesting scenarios. The maturity identification method is based on the DeepLabV3+ network model, which classifies pixel points into four categories: immature, semi-mature, mature, and hypermature. Furthermore, targeted enhancements to the network structure have been incorporated to accommodate the unique maturity characteristics of broccoli. MobileNetV2 contributes to the real-time detection of multiple broccoli heads within the view of camera. The Dense Atrous Spatial Pyramid Pooling (DASPP) module enhances the capability of recognising multiscale features of broccoli, and the Convolutional Block Attention Module (CBAM) further improves the integration of maturity information. The effectiveness of the targeted enhancements has been validated through ablation experiments. The semantic segmentation was successfully applied to broccoli maturity identification for the first time by incorporating a self-designed category determination module. The proposed algorithm achieves a mean intersection over union (mIoU) exceeding 57.9 %, the pixel accuracy (PA) reaching 98.56 %, and the mean category prediction accuracy (mCPA) of 70.93 %. These performance metrics outperform established algorithms such as BASNet, DeepLabV3+, and UNet. This advancement has resulted in an enhancement in the accuracy of maturity identification and a substantial reduction in computational expenses.
abstractGer	The critical technology of the broccoli selective harvesting robot centres around the maturity identification and determination of broccoli heads suitable for harvesting. To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research. This method enables broccoli head detection, pixel-level identification, determination of maturity categories, and precise localisation of suitable heads for harvesting, thus better aligning with practical harvesting scenarios. The maturity identification method is based on the DeepLabV3+ network model, which classifies pixel points into four categories: immature, semi-mature, mature, and hypermature. Furthermore, targeted enhancements to the network structure have been incorporated to accommodate the unique maturity characteristics of broccoli. MobileNetV2 contributes to the real-time detection of multiple broccoli heads within the view of camera. The Dense Atrous Spatial Pyramid Pooling (DASPP) module enhances the capability of recognising multiscale features of broccoli, and the Convolutional Block Attention Module (CBAM) further improves the integration of maturity information. The effectiveness of the targeted enhancements has been validated through ablation experiments. The semantic segmentation was successfully applied to broccoli maturity identification for the first time by incorporating a self-designed category determination module. The proposed algorithm achieves a mean intersection over union (mIoU) exceeding 57.9 %, the pixel accuracy (PA) reaching 98.56 %, and the mean category prediction accuracy (mCPA) of 70.93 %. These performance metrics outperform established algorithms such as BASNet, DeepLabV3+, and UNet. This advancement has resulted in an enhancement in the accuracy of maturity identification and a substantial reduction in computational expenses.
abstract_unstemmed	The critical technology of the broccoli selective harvesting robot centres around the maturity identification and determination of broccoli heads suitable for harvesting. To address this technical challenge, a machine vision method based on semantic segmentation models is proposed in this research. This method enables broccoli head detection, pixel-level identification, determination of maturity categories, and precise localisation of suitable heads for harvesting, thus better aligning with practical harvesting scenarios. The maturity identification method is based on the DeepLabV3+ network model, which classifies pixel points into four categories: immature, semi-mature, mature, and hypermature. Furthermore, targeted enhancements to the network structure have been incorporated to accommodate the unique maturity characteristics of broccoli. MobileNetV2 contributes to the real-time detection of multiple broccoli heads within the view of camera. The Dense Atrous Spatial Pyramid Pooling (DASPP) module enhances the capability of recognising multiscale features of broccoli, and the Convolutional Block Attention Module (CBAM) further improves the integration of maturity information. The effectiveness of the targeted enhancements has been validated through ablation experiments. The semantic segmentation was successfully applied to broccoli maturity identification for the first time by incorporating a self-designed category determination module. The proposed algorithm achieves a mean intersection over union (mIoU) exceeding 57.9 %, the pixel accuracy (PA) reaching 98.56 %, and the mean category prediction accuracy (mCPA) of 70.93 %. These performance metrics outperform established algorithms such as BASNet, DeepLabV3+, and UNet. This advancement has resulted in an enhancement in the accuracy of maturity identification and a substantial reduction in computational expenses.
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title_short	Maturity identification and category determination method of broccoli based on semantic segmentation models
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author2	Li, Dongfang Li, Boliao Zhu, Jianxi Long, Sifang Wang, Jun
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Maturity identification and category determination method of broccoli based on semantic segmentation models

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