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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
20/03/2023 |
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Data da última atualização: |
21/03/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
TORO, A. P. S. G. D. D.; BUENO, I. T.; WERNER, J. P. S.; ANTUNES, J. F. G.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A. |
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Afiliação: |
ANA P. S. G. D. D. TORO, UNIVERSIDADE ESTADUAL DE CAMPINAS; INACIO T. BUENO, UNIVERSIDADE ESTADUAL DE CAMPINAS; JOÃO PAULO SAMPAIO WERNER, UNIVERSIDADE ESTADUAL DE CAMPINAS; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; RUBENS AUGUSTO DE CAMARGO LAMPARELLI, UNIVERSIDADE ESTADUAL DE CAMPINAS; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; PAULO S. G. MAGALHÃES, UNIVERSIDADE ESTADUAL DE CAMPINAS; GLEYCE KELLY DANTAS ARAÚJO FIGUEIREDO, UNIVERSIDADE ESTADUAL DE CAMPINAS. |
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Título: |
SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Remote Sensing, v. 15, n. 4, 1130, Feb. 2023. |
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DOI: |
https://doi.org/10.3390/rs15041130 |
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Idioma: |
Inglês |
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Conteúdo: |
In this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested. |
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Palavras-Chave: |
Agricultura regenerativa; Aprendizado de máquina; Aprendizado profundo; Floresta aleatória; ICLS; Integrated Crop-livestock systems; Long short-term memory; LSTM; Multisource; Random forest; Regenerative agriculture; Sistemas integrados lavoura-pecuária; Transformer. |
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Thesagro: |
Agricultura. |
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Thesaurus Nal: |
Agriculture. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1152495/1/AP-SAR-optical-data-2023.pdf
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Marc: |
LEADER 01762naa a2200409 a 4500
001 2152495
005 2023-03-21
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs15041130$2DOI
100 1 $aTORO, A. P. S. G. D. D.
245 $aSAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.$h[electronic resource]
260 $c2023
520 $aIn this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested.
650 $aAgriculture
650 $aAgricultura
653 $aAgricultura regenerativa
653 $aAprendizado de máquina
653 $aAprendizado profundo
653 $aFloresta aleatória
653 $aICLS
653 $aIntegrated Crop-livestock systems
653 $aLong short-term memory
653 $aLSTM
653 $aMultisource
653 $aRandom forest
653 $aRegenerative agriculture
653 $aSistemas integrados lavoura-pecuária
653 $aTransformer
700 1 $aBUENO, I. T.
700 1 $aWERNER, J. P. S.
700 1 $aANTUNES, J. F. G.
700 1 $aLAMPARELLI, R. A. C.
700 1 $aCOUTINHO, A. C.
700 1 $aESQUERDO, J. C. D. M.
700 1 $aMAGALHÃES, P. S. G.
700 1 $aFIGUEIREDO, G. K. D. A.
773 $tRemote Sensing$gv. 15, n. 4, 1130, Feb. 2023.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
16/11/2017 |
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Data da última atualização: |
09/02/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
OLIVEIRA JÚNIOR, G. A.; CHUD, T. C. S.; VENTURA, R. V.; GARRICK, D. J.; COLE, J. B.; MUNARI, D. P.; FERRAZ, J. B. S.; MULLART, E.; DeNISE, S.; SMITH, S.; SILVA, M. V. G. B. |
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Afiliação: |
Gerson A. Oliveira Júnior, USP; Tatiane C. S. Chud, UNESP; Ricardo V. Ventura, University of Guelph, Guelph, Canada; Dorian J. Garrick, Iowa State University, Ames; John B. Cole, United States Department of Agriculture, Agricultural Research Service, Maryland, USA; Danísio Prado Munari, UNESP Jaboticabal; José B. S. Ferraz, USP; Erik Mullart, CRV Holding B. V., Arnhem, 454, the Netherlands; SUE DeNISE, Zoetis, Kalamazoo, MI; SHANNON SMITH, Zoetis, Kalamazoo, MI; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL. |
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Título: |
Genotype imputation in a tropical crossbred dairy cattle population. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Journal of Dairy Science, v. 100, n. 12, p. 9623-9634, 2017. |
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DOI: |
https://doi.org/10.3168/jds.2017-12732 |
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Idioma: |
Inglês |
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Conteúdo: |
The objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr × Holstein) dairy cattle. The data set consisted of 478 Girolando, 583 Gyr, and 1,198 Holstein sires genotyped at high density with the Illumina BovineHD (Illumina, San Diego, CA) panel, which includes ∼777K markers. The accuracy of imputation from low (20K) and medium densities (50K and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios using different reference populations (RPop) considering Girolando, Gyr, and Holstein breeds separately or combinations of animals of these breeds were tested for imputing genotypes of 166 randomly chosen Girolando animals. The population genotype imputation were performed using FImpute. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper on imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low and medium densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The CORRanim exceeded 0.96 (for 50K and 70K panels) when only Girolando animals were included in RPop (S1). We found smaller CORRanim when Gyr (S2) was used instead of Holstein (S3) as RPop. The same behavior was observed between S4 (Gyr + Girolando) and S5 (Holstein + Girolando) because the target animals were more related to the Holstein population than to the Gyr population. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had a greater effect on accuracy than the purebred haplotypes. All chromosomes had similar imputation accuracies (CORRsnp) within each scenario. Crossbred animals (Girolando) must be included in the reference population to provide the best imputation accuracies. MenosThe objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr × Holstein) dairy cattle. The data set consisted of 478 Girolando, 583 Gyr, and 1,198 Holstein sires genotyped at high density with the Illumina BovineHD (Illumina, San Diego, CA) panel, which includes ∼777K markers. The accuracy of imputation from low (20K) and medium densities (50K and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios using different reference populations (RPop) considering Girolando, Gyr, and Holstein breeds separately or combinations of animals of these breeds were tested for imputing genotypes of 166 randomly chosen Girolando animals. The population genotype imputation were performed using FImpute. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper on imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low and medium densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The CORRanim exceeded 0.96 (for 50K and 70K panels) when only Girolando animals were included in RPop (S1). We found smaller CORRanim when Gyr (S2) was used instead of Holstein (S3) as RPop. The same behavior was obse... Mostrar Tudo |
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Palavras-Chave: |
Impute. |
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Thesaurus NAL: |
genotype; single nucleotide polymorphism. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 03028naa a2200289 a 4500
001 2079937
005 2024-02-09
008 2017 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3168/jds.2017-12732$2DOI
100 1 $aOLIVEIRA JÚNIOR, G. A.
245 $aGenotype imputation in a tropical crossbred dairy cattle population.$h[electronic resource]
260 $c2017
520 $aThe objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr × Holstein) dairy cattle. The data set consisted of 478 Girolando, 583 Gyr, and 1,198 Holstein sires genotyped at high density with the Illumina BovineHD (Illumina, San Diego, CA) panel, which includes ∼777K markers. The accuracy of imputation from low (20K) and medium densities (50K and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios using different reference populations (RPop) considering Girolando, Gyr, and Holstein breeds separately or combinations of animals of these breeds were tested for imputing genotypes of 166 randomly chosen Girolando animals. The population genotype imputation were performed using FImpute. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper on imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low and medium densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The CORRanim exceeded 0.96 (for 50K and 70K panels) when only Girolando animals were included in RPop (S1). We found smaller CORRanim when Gyr (S2) was used instead of Holstein (S3) as RPop. The same behavior was observed between S4 (Gyr + Girolando) and S5 (Holstein + Girolando) because the target animals were more related to the Holstein population than to the Gyr population. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had a greater effect on accuracy than the purebred haplotypes. All chromosomes had similar imputation accuracies (CORRsnp) within each scenario. Crossbred animals (Girolando) must be included in the reference population to provide the best imputation accuracies.
650 $agenotype
650 $asingle nucleotide polymorphism
653 $aImpute
700 1 $aCHUD, T. C. S.
700 1 $aVENTURA, R. V.
700 1 $aGARRICK, D. J.
700 1 $aCOLE, J. B.
700 1 $aMUNARI, D. P.
700 1 $aFERRAZ, J. B. S.
700 1 $aMULLART, E.
700 1 $aDeNISE, S.
700 1 $aSMITH, S.
700 1 $aSILVA, M. V. G. B.
773 $tJournal of Dairy Science$gv. 100, n. 12, p. 9623-9634, 2017.
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