Weeds Classification with Deep Learning: An Investigation Using CNN, Vision Transformers, Pyramid Vision Transformers, and Ensemble Strategy

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Porto (FEUP)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorRozendo, Guilherme Botazzo-
Autor(es): dc.creatorRoberto, Guilherme Freire-
Autor(es): dc.creatordo Nascimento, Marcelo Zanchetta-
Autor(es): dc.creatorAlves Neves, Leandro-
Autor(es): dc.creatorLumini, Alessandra-
Data de aceite: dc.date.accessioned2025-08-21T22:37:27Z-
Data de disponibilização: dc.date.available2025-08-21T22:37:27Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-49018-7_17-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307220-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307220-
Descrição: dc.descriptionWeeds are a significant threat to agricultural production. Weed classification systems based on image analysis have offered innovative solutions to agricultural problems, with convolutional neural networks (CNNs) playing a pivotal role in this task. However, CNNs are limited in their ability to capture global relationships in images due to their localized convolutional operation. Vision Transformers (ViT) and Pyramid Vision Transformers (PVT) have emerged as viable solutions to overcome this limitation. Our study aims to determine the effectiveness of CNN, PVT, and ViT in classifying weeds in image datasets. We also examine if combining these methods in an ensemble can enhance classification performance. Our tests were conducted on significant agricultural datasets, including DeepWeeds and CottonWeedID15. The results indicate that a maximum of 3 methods in an ensemble, with only 15 epochs in training, can achieve high accuracy rates of up to 99.17%. This study demonstrates that high accuracies can be achieved with ease of implementation and only a few epochs.-
Descrição: dc.descriptionEuropean Commission-
Descrição: dc.descriptionDepartment of Computer Science and Engineering (DISI) - University of Bologna-
Descrição: dc.descriptionFaculty of Engineering University of Porto (FEUP)-
Descrição: dc.descriptionFaculty of Computer Science (FACOM) Federal University of Uberlândia (UFU)-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University-
Formato: dc.format229-243-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCNN-
Palavras-chave: dc.subjectEnsemble-
Palavras-chave: dc.subjectPyramid Vision Transformers-
Palavras-chave: dc.subjectVision transformers-
Palavras-chave: dc.subjectWeeds classification-
Título: dc.titleWeeds Classification with Deep Learning: An Investigation Using CNN, Vision Transformers, Pyramid Vision Transformers, and Ensemble Strategy-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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