Grapevine reconstruction and pruning points identification based on Deep Learning

Despite the growing prevalence of robotics in agriculture, there is still a limited amount of research specifically targeting the automation of grapevine management. Due to the complexity, the pruning task during the dormant season demands skilled laborers, who are increasingly scarce during the win...

Descripción completa

Detalles Bibliográficos
Autor: Wang, Yiyi
Tipo de recurso: tesis de maestría
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/413047
Acceso en línea:https://hdl.handle.net/2117/413047
Access Level:acceso abierto
Palabra clave:Grapes-- Pruning--Automation
Vinya--Poda--Automatització
Àrees temàtiques de la UPC::Enginyeria agroalimentària::Agricultura::Viticultura
id ES_9bfc53d3bb3243a23d8d9935c92bbef2
oai_identifier_str oai:upcommons.upc.edu:2117/413047
network_acronym_str ES
network_name_str España
repository_id_str
spelling Grapevine reconstruction and pruning points identification based on Deep LearningWang, YiyiGrapes-- Pruning--AutomationVinya--Poda--AutomatitzacióÀrees temàtiques de la UPC::Enginyeria agroalimentària::Agricultura::ViticulturaDespite the growing prevalence of robotics in agriculture, there is still a limited amount of research specifically targeting the automation of grapevine management. Due to the complexity, the pruning task during the dormant season demands skilled laborers, who are increasingly scarce during the winter months. For the potential prospect, CANOPIES, a H2020 European Project, seeks to pioneer a new collaborative approach between humans and robots in precision agriculture, specifically targeting permanent crops such as table-grape vineyards. Its goal is to enable farm workers to seamlessly collaborate with robot teams to carry out harvesting and pruning more efficiently and effectively. Aiming to fulfill the need for winter pruning task for the CANOPIES project, this master’s thesis presents a novel design of perception in the pruning procedure. The algorithm is mainly developed based on deep learning, providing selective solutions for different application scenarios for the tasks such as plant reconstruction, vineyards organ segmentation and potential pruning point identification. The developed system highly satisfies the need of CANOPIES project for winter pruning. In plant reconstruction section, several models with satisfactory performance are obtained whose best mIoU is 88.72%, and shortest computational cost is 0.1124 seconds. While in the part of vine organ segmentation, the best network gets mPA50 of 60.43% also with a high speed. Finally, a node graph is generated to show the topological structure of the relationship between different organs along with pruning point localization.Universitat Politècnica de CatalunyaGrau Saldes, Antoni20242024-05-1620242024-07-29master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/413047reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4130472026-05-27T15:37:01Z
dc.title.none.fl_str_mv Grapevine reconstruction and pruning points identification based on Deep Learning
title Grapevine reconstruction and pruning points identification based on Deep Learning
spellingShingle Grapevine reconstruction and pruning points identification based on Deep Learning
Wang, Yiyi
Grapes-- Pruning--Automation
Vinya--Poda--Automatització
Àrees temàtiques de la UPC::Enginyeria agroalimentària::Agricultura::Viticultura
title_short Grapevine reconstruction and pruning points identification based on Deep Learning
title_full Grapevine reconstruction and pruning points identification based on Deep Learning
title_fullStr Grapevine reconstruction and pruning points identification based on Deep Learning
title_full_unstemmed Grapevine reconstruction and pruning points identification based on Deep Learning
title_sort Grapevine reconstruction and pruning points identification based on Deep Learning
dc.creator.none.fl_str_mv Wang, Yiyi
author Wang, Yiyi
author_facet Wang, Yiyi
author_role author
dc.contributor.none.fl_str_mv Grau Saldes, Antoni
dc.subject.none.fl_str_mv Grapes-- Pruning--Automation
Vinya--Poda--Automatització
Àrees temàtiques de la UPC::Enginyeria agroalimentària::Agricultura::Viticultura
topic Grapes-- Pruning--Automation
Vinya--Poda--Automatització
Àrees temàtiques de la UPC::Enginyeria agroalimentària::Agricultura::Viticultura
description Despite the growing prevalence of robotics in agriculture, there is still a limited amount of research specifically targeting the automation of grapevine management. Due to the complexity, the pruning task during the dormant season demands skilled laborers, who are increasingly scarce during the winter months. For the potential prospect, CANOPIES, a H2020 European Project, seeks to pioneer a new collaborative approach between humans and robots in precision agriculture, specifically targeting permanent crops such as table-grape vineyards. Its goal is to enable farm workers to seamlessly collaborate with robot teams to carry out harvesting and pruning more efficiently and effectively. Aiming to fulfill the need for winter pruning task for the CANOPIES project, this master’s thesis presents a novel design of perception in the pruning procedure. The algorithm is mainly developed based on deep learning, providing selective solutions for different application scenarios for the tasks such as plant reconstruction, vineyards organ segmentation and potential pruning point identification. The developed system highly satisfies the need of CANOPIES project for winter pruning. In plant reconstruction section, several models with satisfactory performance are obtained whose best mIoU is 88.72%, and shortest computational cost is 0.1124 seconds. While in the part of vine organ segmentation, the best network gets mPA50 of 60.43% also with a high speed. Finally, a node graph is generated to show the topological structure of the relationship between different organs along with pruning point localization.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-05-16
2024
2024-07-29
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/413047
url https://hdl.handle.net/2117/413047
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869414618198179840
score 15,81155

Similares en LA Referencia