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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/08/2025 |
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Data da última atualização: |
07/08/2025 |
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Autoria: |
TOMÀS, J. C.; FARIA, F. A.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; MEDEIROS, C. B. |
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Afiliação: |
JORDI CREUS TOMÀS, UNIVERSIDADE ESTADUAL DE CAMPINAS; FABIO AUGUSTO FARIA, UNIVERSIDADE FEDERAL DE SÃO PAULO; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; CLAUDIA BAUZER MEDEIROS, UNIVERSIDADE ESTADUAL DE CAMPINAS. |
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Título: |
SiRCub: a novel approach to recognize agricultural crops using supervised classification. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
In: INFORMATION RESOURCES MANAGEMENT ASSOCIATION (ed.). Environmental information systems: concepts, methodologies, tools, and applications. Hershey: IGI Global, 2019. v. II, chap. 50, p. 1129-1147. |
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DOI: |
10.4018/978-1-5225-7033-2.ch050. |
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Idioma: |
Inglês |
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Conteúdo: |
This paper presents a new approach to deal with agricultural crop recognition using SVM (Support Vector Machine), applied to time series of NDVI images. The presented method can be divided into two steps. First, the Timesat software package is used to extract a set of crop features from the NDVI time series. These features serve as descriptors that characterize each NDVI vegetation curve, i.e., the period comprised between sowing and harvesting dates. Then, it is used an SVM to learn the patterns that define each type of crop, and create a crop model that allows classifying new series. The authors present a set of experiments that show the effectiveness of this technique. They evaluated their algorithm with a collection of more than 3000 time series from the Brazilian State of Mato Grosso spanning 4 years (2009-2013). Such time series were annotated in the field by specialists from Embrapa (Brazilian Agricultural Research Corporation). This methodology is generic, and can be adapted to distinct regions and crop profiles. |
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Palavras-Chave: |
Aprendizado de máquina; Crop classification; Enhanced Vegetation Index; Índice de vegetação; LULC; Machine Learning; Séries temporais; SIRCub; Support Vector Machine. |
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Thesagro: |
Sensoriamento Remoto; Uso da Terra. |
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Thesaurus Nal: |
Artificial intelligence; Land cover; Land use; Normalized difference vegetation index; Remote sensing; Time series analysis; Vegetation index. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02377naa a2200397 a 4500
001 2177764
005 2025-08-07
008 2019 bl uuuu u00u1 u #d
024 7 $a10.4018/978-1-5225-7033-2.ch050.$2DOI
100 1 $aTOMÀS, J. C.
245 $aSiRCub$ba novel approach to recognize agricultural crops using supervised classification.$h[electronic resource]
260 $c2019
520 $aThis paper presents a new approach to deal with agricultural crop recognition using SVM (Support Vector Machine), applied to time series of NDVI images. The presented method can be divided into two steps. First, the Timesat software package is used to extract a set of crop features from the NDVI time series. These features serve as descriptors that characterize each NDVI vegetation curve, i.e., the period comprised between sowing and harvesting dates. Then, it is used an SVM to learn the patterns that define each type of crop, and create a crop model that allows classifying new series. The authors present a set of experiments that show the effectiveness of this technique. They evaluated their algorithm with a collection of more than 3000 time series from the Brazilian State of Mato Grosso spanning 4 years (2009-2013). Such time series were annotated in the field by specialists from Embrapa (Brazilian Agricultural Research Corporation). This methodology is generic, and can be adapted to distinct regions and crop profiles.
650 $aArtificial intelligence
650 $aLand cover
650 $aLand use
650 $aNormalized difference vegetation index
650 $aRemote sensing
650 $aTime series analysis
650 $aVegetation index
650 $aSensoriamento Remoto
650 $aUso da Terra
653 $aAprendizado de máquina
653 $aCrop classification
653 $aEnhanced Vegetation Index
653 $aÍndice de vegetação
653 $aLULC
653 $aMachine Learning
653 $aSéries temporais
653 $aSIRCub
653 $aSupport Vector Machine
700 1 $aFARIA, F. A.
700 1 $aESQUERDO, J. C. D. M.
700 1 $aCOUTINHO, A. C.
700 1 $aMEDEIROS, C. B.
773 $tIn: INFORMATION RESOURCES MANAGEMENT ASSOCIATION (ed.). Environmental information systems: concepts, methodologies, tools, and applications. Hershey: IGI Global, 2019.$gv. II, chap. 50, p. 1129-1147.
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Embrapa Agricultura Digital (CNPTIA) |
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| 1. |  | ALVES, B. R. C.; CARDOSO, R. C.; DOAN, R.; Gary L. Williams; DINDOT, S. V.; AMSTALDEN, M. Nutritional Programming of Accelerated Puberty in Heifers: Alterations in DNA Methylation in the Arcuate Nucleus. In: ANNUAL MEETING OF THE SOCIETY FOR THE STUDY OF REPRODUCTION, 48., 2015, San Juan, Puerto Rico, USA. Evolution of sex: abstracts. San Juan: SSR, 2015. p. 117| Tipo: Resumo em Anais de Congresso |
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