Vineyards monitoring using convolutional neural networks and multispectral images

Bibliographic Details
Main Author: Ferreira, Rodrigo Manso Teixeira Basílio
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.5/96898
Summary: Tese de mestrado, Engenharia Informática, 2024, Universidade de Lisboa, Faculdade de Ciências
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network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling Vineyards monitoring using convolutional neural networks and multispectral imagesAgricultura de precisãoDeteção de vinhasSegmentação bináriaArquitetura U-NetDeteção de linhas de cultivoTeses de mestrado - 2024Departamento de InformáticaTese de mestrado, Engenharia Informática, 2024, Universidade de Lisboa, Faculdade de CiênciasThis study presents the development of an agricultural monitoring system designed to detect vineyards and crop lines through the application of binary segmentation techniques. The primary objective is to enhance the efficiency of vineyard monitoring, enabling precise plant detection using aerial imagery captured by unmanned aerial vehicles (UAVs). The system utilizes U-Net architecture for semantic segmentation, which was selected for its ability to effectively differentiate between vine and non-vine areas, promoting resource optimization and sustainable viticulture. Additionally, an algorithm based on the Hough Transform was implemented to accurately detect vineyard crop rows, further supporting precision agriculture practices. The model was trained and validated using datasets obtained from various sources, including publicly available datasets and those provided by industry partners. Evaluation metrics such as accuracy, Intersection over Union (IoU), and Dice Coefficient were employed to assess model performance, with results indicating varying levels of success across different datasets. The research contributes to the growing field of precision agriculture by offering a practical tool for vineyard management, with potential applications in resource allocation, environmental sustainability, and operational efficiency. The system’s design and the methodologies employed underscore the feasibility of integrating advanced machine learning models into real-world agricultural contexts.The code and dataset are publicly https://github.com/rodrigo-99ferreira/VineyardsGarcia, Nuno Ricardo da CruzCarvalho, João Pedro Leal Abalada de MatosRepositório da Universidade de LisboaFerreira, Rodrigo Manso Teixeira Basílio2025-01-07T10:23:54Z202420242024-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.5/96898TID:203876857enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-17T16:31:16Zoai:repositorio.ulisboa.pt:10400.5/96898Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T04:18:01.066664Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Vineyards monitoring using convolutional neural networks and multispectral images
title Vineyards monitoring using convolutional neural networks and multispectral images
spellingShingle Vineyards monitoring using convolutional neural networks and multispectral images
Ferreira, Rodrigo Manso Teixeira Basílio
Agricultura de precisão
Deteção de vinhas
Segmentação binária
Arquitetura U-Net
Deteção de linhas de cultivo
Teses de mestrado - 2024
Departamento de Informática
title_short Vineyards monitoring using convolutional neural networks and multispectral images
title_full Vineyards monitoring using convolutional neural networks and multispectral images
title_fullStr Vineyards monitoring using convolutional neural networks and multispectral images
title_full_unstemmed Vineyards monitoring using convolutional neural networks and multispectral images
title_sort Vineyards monitoring using convolutional neural networks and multispectral images
author Ferreira, Rodrigo Manso Teixeira Basílio
author_facet Ferreira, Rodrigo Manso Teixeira Basílio
author_role author
dc.contributor.none.fl_str_mv Garcia, Nuno Ricardo da Cruz
Carvalho, João Pedro Leal Abalada de Matos
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Ferreira, Rodrigo Manso Teixeira Basílio
dc.subject.por.fl_str_mv Agricultura de precisão
Deteção de vinhas
Segmentação binária
Arquitetura U-Net
Deteção de linhas de cultivo
Teses de mestrado - 2024
Departamento de Informática
topic Agricultura de precisão
Deteção de vinhas
Segmentação binária
Arquitetura U-Net
Deteção de linhas de cultivo
Teses de mestrado - 2024
Departamento de Informática
description Tese de mestrado, Engenharia Informática, 2024, Universidade de Lisboa, Faculdade de Ciências
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024-01-01T00:00:00Z
2025-01-07T10:23:54Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.5/96898
TID:203876857
url http://hdl.handle.net/10400.5/96898
identifier_str_mv TID:203876857
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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