Detalhes bibliográficos
| Ano de defesa: |
2024 |
| Autor(a) principal: |
Kai, Priscila Marques
 |
| Orientador(a): |
Costa, Ronaldo Martins da
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| Banca de defesa: |
Costa, Ronaldo Martins da,
Soares, Fabrízzio Alphonsus Alves de Melo Nunes,
Leitão Júnior, Plínio de Sá,
Arraut, Eduardo Moraes,
Costa, Kelton Augusto Pontara da |
| Tipo de documento: |
Tese
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| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal de Goiás
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| Programa de Pós-Graduação: |
Programa de Pós-graduação em Ciência da Computação (INF)
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| Departamento: |
Instituto de Informática - INF (RMG)
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| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://repositorio.bc.ufg.br/tede/handle/tede/13788
|
Resumo: |
The classification of different crop varieties still faces significant challenges due to their similar spectral characteristics. To address this issue, the integration of remote sensing techniques with deep learning methods offers a promising solution by analyzing pixel-level data based on spectral bands, band combinations, and vegetation indices. In this study, we developed a cross-deep neural network methodology, referred to as DCN-S, with a case study focused on the classification of sugarcane varieties. The methodology was applied to remote sensing data from cultivation areas in the state of Goiás, Brazil, collected between 2019 and 2021. The DCN-S model was compared with traditional classifiers, such as k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Random Forest, as well as other neural network configurations. The results indicated that the DCN-S model achieved competitive accuracy in validation scenarios, including temporal variety considerations when compared to other studies in the literature. Moreover, the model excelled in classifying varieties without requiring the separation of developmental stages, surpassing traditional methods. Performance improvements were further observed after applying a voting process. Finally, this work’s main contributions include developing an approach for classifying agricultural varieties by combining deep learning with remote sensing data and validating this methodology in a practical scenario. The results highlight the potential of the DCN-S model to outperform traditional techniques, offering a tool for automated agricultural monitoring |
