Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features

The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the ai...
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Autor*in:	Yuanhong Li [verfasserIn] Jiapeng Liao [verfasserIn] Jing Wang [verfasserIn] Yangfan Luo [verfasserIn] Yubin Lan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	gradient distribution lychee instance segmentation mask fault-tolerance

Übergeordnetes Werk:	In: Agronomy - MDPI AG, 2012, 13(2023), 2435, p 2435
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:2435, p 2435

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agronomy13092435

Katalog-ID:	DOAJ09347105X

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520			\|a The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP<sup<3</sup<Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP<sup<3</sup<Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters.
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allfields	10.3390/agronomy13092435 doi (DE-627)DOAJ09347105X (DE-599)DOAJ2bce71f4b321413fa504d790d38d897f DE-627 ger DE-627 rakwb eng Yuanhong Li verfasserin aut Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP<sup<3</sup<Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP<sup<3</sup<Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. gradient distribution lychee instance segmentation mask fault-tolerance Agriculture S Jiapeng Liao verfasserin aut Jing Wang verfasserin aut Yangfan Luo verfasserin aut Yubin Lan verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 2435, p 2435 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:2435, p 2435 https://doi.org/10.3390/agronomy13092435 kostenfrei https://doaj.org/article/2bce71f4b321413fa504d790d38d897f kostenfrei https://www.mdpi.com/2073-4395/13/9/2435 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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 13 2023 2435, p 2435
spelling	10.3390/agronomy13092435 doi (DE-627)DOAJ09347105X (DE-599)DOAJ2bce71f4b321413fa504d790d38d897f DE-627 ger DE-627 rakwb eng Yuanhong Li verfasserin aut Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP<sup<3</sup<Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP<sup<3</sup<Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. gradient distribution lychee instance segmentation mask fault-tolerance Agriculture S Jiapeng Liao verfasserin aut Jing Wang verfasserin aut Yangfan Luo verfasserin aut Yubin Lan verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 2435, p 2435 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:2435, p 2435 https://doi.org/10.3390/agronomy13092435 kostenfrei https://doaj.org/article/2bce71f4b321413fa504d790d38d897f kostenfrei https://www.mdpi.com/2073-4395/13/9/2435 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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 13 2023 2435, p 2435
allfields_unstemmed	10.3390/agronomy13092435 doi (DE-627)DOAJ09347105X (DE-599)DOAJ2bce71f4b321413fa504d790d38d897f DE-627 ger DE-627 rakwb eng Yuanhong Li verfasserin aut Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP<sup<3</sup<Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP<sup<3</sup<Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. gradient distribution lychee instance segmentation mask fault-tolerance Agriculture S Jiapeng Liao verfasserin aut Jing Wang verfasserin aut Yangfan Luo verfasserin aut Yubin Lan verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 2435, p 2435 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:2435, p 2435 https://doi.org/10.3390/agronomy13092435 kostenfrei https://doaj.org/article/2bce71f4b321413fa504d790d38d897f kostenfrei https://www.mdpi.com/2073-4395/13/9/2435 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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 13 2023 2435, p 2435
allfieldsGer	10.3390/agronomy13092435 doi (DE-627)DOAJ09347105X (DE-599)DOAJ2bce71f4b321413fa504d790d38d897f DE-627 ger DE-627 rakwb eng Yuanhong Li verfasserin aut Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP<sup<3</sup<Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP<sup<3</sup<Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. gradient distribution lychee instance segmentation mask fault-tolerance Agriculture S Jiapeng Liao verfasserin aut Jing Wang verfasserin aut Yangfan Luo verfasserin aut Yubin Lan verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 2435, p 2435 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:2435, p 2435 https://doi.org/10.3390/agronomy13092435 kostenfrei https://doaj.org/article/2bce71f4b321413fa504d790d38d897f kostenfrei https://www.mdpi.com/2073-4395/13/9/2435 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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 13 2023 2435, p 2435
allfieldsSound	10.3390/agronomy13092435 doi (DE-627)DOAJ09347105X (DE-599)DOAJ2bce71f4b321413fa504d790d38d897f DE-627 ger DE-627 rakwb eng Yuanhong Li verfasserin aut Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP<sup<3</sup<Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP<sup<3</sup<Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. gradient distribution lychee instance segmentation mask fault-tolerance Agriculture S Jiapeng Liao verfasserin aut Jing Wang verfasserin aut Yangfan Luo verfasserin aut Yubin Lan verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 2435, p 2435 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:2435, p 2435 https://doi.org/10.3390/agronomy13092435 kostenfrei https://doaj.org/article/2bce71f4b321413fa504d790d38d897f kostenfrei https://www.mdpi.com/2073-4395/13/9/2435 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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 13 2023 2435, p 2435
language	English
source	In Agronomy 13(2023), 2435, p 2435 volume:13 year:2023 number:2435, p 2435
sourceStr	In Agronomy 13(2023), 2435, p 2435 volume:13 year:2023 number:2435, p 2435
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topic_facet	gradient distribution lychee instance segmentation mask fault-tolerance Agriculture S
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authorswithroles_txt_mv	Yuanhong Li @@aut@@ Jiapeng Liao @@aut@@ Jing Wang @@aut@@ Yangfan Luo @@aut@@ Yubin Lan @@aut@@
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However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP<sup<3</sup<Net). 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author	Yuanhong Li
spellingShingle	Yuanhong Li misc gradient distribution misc lychee misc instance segmentation misc mask misc fault-tolerance misc Agriculture misc S Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features
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topic_title	Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features gradient distribution lychee instance segmentation mask fault-tolerance
topic	misc gradient distribution misc lychee misc instance segmentation misc mask misc fault-tolerance misc Agriculture misc S
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title	Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features
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title_full	Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features
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title_sort	prototype network for predicting occluded picking position based on lychee phenotypic features
title_auth	Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features
abstract	The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP<sup<3</sup<Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP<sup<3</sup<Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters.
abstractGer	The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP<sup<3</sup<Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP<sup<3</sup<Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters.
abstract_unstemmed	The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP<sup<3</sup<Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP<sup<3</sup<Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters.
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title_short	Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features
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