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| Registros recuperados : 15 | |
| 2. |  | FREIRE, R. M. M.; FARIAS, S. R. de; NARAIN, N.; MOREIRA, R. de A.; SANTOS, R. C. dos. Aminoacido e acidos graxos em genotipos de amendoim do grupo virginia. Revista de Oleaginosas e Fibrosas, Campina Grande, v.5, n.1, p.273-281,Jan-Abr. 2002.| Biblioteca(s): Embrapa Algodão. |
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| 4. | | FREIRE, R. M. M.; NARAIN, N.; MOREIRA, R. de A.; SANTOS, R. C. dos; FARIAS, S. R. de; QUEIROZ, M. do S. R. de. Avaliacao proteica da farinha desengordurada de genotipos de amendoim. Revista Oleaginosa e Fibrasas, v.4, n.3, p.193-199, ago-dez, 2000.| Biblioteca(s): Embrapa Algodão. |
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| 5. | | FREIRE, R. M. M.; NARAIN, N.; MOREIRA, R. de A.; SANTOS, R. C. dos; FARIAS, S. R. de; QUEIROZ, M. do S. R. Avaliacao protetica da farinha desengordurada de genotipos de amendoim. In: CONGRESSO BRASILEIRO DE CIENCIA E TECNOLOGIA DE ALIMENTOS,17.,2000,Fortaleza.Livro de resumos.Fortaleza.SBCTA, 2000.P.540.| Biblioteca(s): Embrapa Algodão. |
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| 6. | | AGUIAR, R. P. de; MIRANDA, M. R. A. de; OLIVEIRA, L. de S.; MOSCA, J. L.; MOREIRA, R. de A.; ENÉAS FILHO, J. Conservação pós-colheita e atividade de enzimas antioxidantes de mangas 'tommy atkins' recobertas por película de galactomana. In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 20.; ANNUAL MEETING OF THE INTERAMERICAN SOCIETY FOR TROPICAL AGRICULTURE, 54., 2008, Vitória. Livro de resumos. Vitória: Incaper, 2008.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 7. | | MENEZES, R. de C. A. A.; VIEIRA, L. da S.; CAVALCANTE, A. C. R.; CAVADA, B. S.; OLIVEIRA, J. T. A.; MOREIRA, R. de A. "In vitro" preliminary studies of ovicidal effects of leaves and seeds from four legumes upon Haemonchus contortus of goats. In: CONGRESSO MUNDIAL DE VETERINÁRIA, 24., 1991, Rio de Janeiro. Resumos... Rio de Janeiro: Sadia: FINEP: CNPq, 1991. p. 57. Ref. 3.1.2.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 8. | | MENDES, L. G.; MENDES, F. R. da S.; FURTADO, R. F.; BASTOS, M. do S. R.; OLIVEIRA, M. de A.; COSTA, J. M. C. da; CHENG, H. N.; BISWAS, A.; MOREIRA, R. de A. Cashew gum-galactomannan blends for rosemary essential oil encapsulation. In: BARBOSA NETO, O.; PRAXEDES, M. F. da S.; SILVA, P. F. da; CARVALHO JÚNIOR, F. F. de (org.). Ciências Biológicas e da Saúde: integrando saberes em diferentes contextos. São Paulo: Científica Digital, 2023. cap. 5. v. 3 p. 73-88. Autoria: (Huain [i.e. Huai] Nan Cheng).| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 9. | | HOLANDA, D. K. R.; WURLITZER, N. J.; DIONISIO, A. P.; BARROS, A. R. C.; MOREIRA, R. DE A.; SOUSA, P. H. M. DE; BRITO, E. S. de; RIBEIRO, P. R. V.; COSTA, A. M.; IUNES, M. F. Garlic passion fruit (Passiflora tenuifila Killip): assessment of eventual acute toxicity, anxiolytic, sedative, and anticonvulsant effects using in vivo assays. Food Research International, Amsterdam, v. 128, artigo 108813, 8 p. Feb. 2020.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 10. | | ALEXANDRE, J. DE B.; BARROSO, T. L. C. T.; OLIVEIRA, M. DE A.; MENDES, F. R. DA S.; COSTA, J. M. C. DA; MOREIRA, R. DE A.; FURTADO, R. F. Cross-linked coacervates of cashew gum and gelatin in the encapsulation of pequi oil. Ciência Rural, Santa Maria, v. 49, n. 12, artigo e20190079, 12 p. 2019.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 11. | | SILVA, V. P. A. da; FURTADO, R. F.; CARVALHO, J. B. de; PIMENTA, G. R.; ALVES, C. R.; MOREIRA, R. de A.; MOREIRA, A. C. de O. M.; DUTRA, R. A. F. Purificação de lectinas de torta de mamona utilizando cromatografia de afinidade de matrizes de goma de guar e xiloglucana. In: CONGRESSO BRASILEIRO DE MAMONA, 4.; SIMPÓSIO INTERNACIONAL DE OLEAGINOSAS ENERGÉTICAS, 1., 2010, João Pessoa. Inclusão social e energia: anais. Campina Grande: Embrapa Algodão, 2010.| Biblioteca(s): Embrapa Algodão. |
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| 12. | | LOVERA, M.; CASTRO, G. M. C. D.; PIRES, N. da R.; BASTOS, M. do S. R.; HOLANDA-ARAÚJO, M. L.; LAURENTIN, A.; MOREIRA, R. de A.; OLIVEIRA, H. D. de. Pyrodextrinization of yam (Dioscorea sp.) starch isolated from tubers grown in Brazil and physicochemical characterization of yellow pyrodextrins. Carbohydrate Polymers, v. 242, art. no. 116382, 2020.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 13. | | HOLANDA, D. K. R.; WURLITZER, N. J.; DIONISIO, A. P.; CAMPOS, A. R.; BRITO, E. S. de; SILVA, L. M. A. e; RIBEIRO, P. R. V.; COSTA, A. M.; SOUZA, P. H. M.; LIMA. F. A. V.; MOREIRA, R. de A. Passiflora tenuifila Killip: Assessment of chemical composition by 1H NMR and UPLC-ESI-Q-TOF-MSE and its bioactive properties in a rotenone-induced rat model of Parkinson's disease. Journal of Functional Foods, v. 62, art. 103529, 2019. 9 p.| Biblioteca(s): Embrapa Agroindústria Tropical; Embrapa Cerrados. |
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| 14. | | HOLANDA, D. K. R.; WURLITZER, N. J.; DIONISIO, A. P.; BARROS, A. R. C.; BRITO, E. S. de; SILVA, L. M. A. e; RIBEIRO, P. R. V.; COSTA, A. M.; SOUZA, P. H. M. DE; LIMA, F. A. V.; MOREIRA, R. DE A. Passiflora tenuifila Killip: assessment of chemical composition by ¹H NMR and UPLC-ESI-Q-TOF-MSe and its bioactive properties in a rotenone-induced rat model of Parkinson's disease. Journal of Functional Foods, Amsterdam, v. 62, artigo 103529, p. 1-9, Nov. 2019.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 15. | | BEZERRA JUNIOR, R. Q.; ELOY, A. M. X.; FURTADO, J. R.; PINHEIRO, R. R.; ANDRIOLI, A.; MORENO, F. B.; LOBO, M. D. P.; MONTEIRO-MOREIRA, A. C. O.; MOREIRA, R. de A.; PINTO, T. M. F.; TEIXEIRA, M. F. da S. A panel of protein candidates for comprehensive study of Caprine Arthritis Encephalitis (CAE) infection. Tropical Animal Health and Production, v. 50, n. 1, n. 7, p. 43-48, Oct. 2018.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| Registros recuperados : 15 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Corte. Para informações adicionais entre em contato com cnpgc.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Corte. |
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Data corrente: |
12/12/2023 |
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Data da última atualização: |
12/12/2023 |
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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: |
BRETAS, I. L.; VALENTE, D. S. M.; OLIVEIRA, T. F. DE; MONTAGNER, D. B.; EUCLIDES, V. P. B.; CHIZZOTTI, F. H. M. |
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Afiliação: |
IGOR LIMA BRETAS, UNIVERSIDADE FEDERAL DE VIÇOSA; DOMINGOS SARVIO MAGALHÃES VALENTE, UNIVERSIDADE FEDERAL DE VIÇOSA; THIAGO FURTADO DE OLIVEIRA, UNIVERSIDADE FEDERAL DE VIÇOSA; DENISE BAPTAGLIN MONTAGNER, CNPGC; VALERIA PACHECO BATISTA EUCLIDES, CNPGC; FERNANDA HELENA MARTINS CHIZZOTTI, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Canopy height and biomass prediction in Mombaça guinea grass pastures using satellite imagery and machine learning. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Precision Agriculture, v. 24, n. 4, p. 1638–1662, 2023. |
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DOI: |
https://doi.org/10.1007/s11119-023-10013-z |
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Idioma: |
Inglês |
|
Notas: |
Published online: 17 April 2023. |
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Conteúdo: |
ABSTRACT - Remote sensing can serve as a promising solution for monitoring spatio-temporal variability in grasslands, providing timely information about diferent biophysical parameters. We aimed to develop models for canopy height classifcation and aboveground biomass estimation in pastures of Megathyrsus maximus cv. Mombaça using machine learning techniques and images obtained from the Sentinel-2 satellite. We used diferent spectral bands from the Sentinel-2, which were obtained and processed entirely in the cloud computing space. Three canopy height classes were defned according to grazing management recommendations: Class 0 (<0.45 m), Class 1 (0.45–0.80 m) and Class 2 (>0.80 m). For modeling, the original database was divided into training data (85%) and test data (15%). To avoid dependency between our training and test datasets and ensure greater generalization capacity, we used a spatial grouping evaluation structure. The random forest algorithm was used to predict canopy height and aboveground biomass by using height and biomass feld reference data obtained from 54 paddocks in Brazil between 2016 and 2018. Our results demonstrated precision, recall, and accuracy values of up to 73%, 73%, and 72%, respectively, for canopy height classifcation. In addition, the models showed reasonable predictive performance for aboveground fresh biomass (AFB) and dry matter concentration (DMC; R2=0.61 and 0.69, respectively). We conclude that the combined use of satellite imagery and machine learning techniques has potential to predict canopy height and aboveground biomass of Megathyrsus maximus cv. Mombaça. However, further studies should be conducted to improve the proposed models and develop software to implement the tool under feld conditions. MenosABSTRACT - Remote sensing can serve as a promising solution for monitoring spatio-temporal variability in grasslands, providing timely information about diferent biophysical parameters. We aimed to develop models for canopy height classifcation and aboveground biomass estimation in pastures of Megathyrsus maximus cv. Mombaça using machine learning techniques and images obtained from the Sentinel-2 satellite. We used diferent spectral bands from the Sentinel-2, which were obtained and processed entirely in the cloud computing space. Three canopy height classes were defned according to grazing management recommendations: Class 0 (<0.45 m), Class 1 (0.45–0.80 m) and Class 2 (>0.80 m). For modeling, the original database was divided into training data (85%) and test data (15%). To avoid dependency between our training and test datasets and ensure greater generalization capacity, we used a spatial grouping evaluation structure. The random forest algorithm was used to predict canopy height and aboveground biomass by using height and biomass feld reference data obtained from 54 paddocks in Brazil between 2016 and 2018. Our results demonstrated precision, recall, and accuracy values of up to 73%, 73%, and 72%, respectively, for canopy height classifcation. In addition, the models showed reasonable predictive performance for aboveground fresh biomass (AFB) and dry matter concentration (DMC; R2=0.61 and 0.69, respectively). We conclude that the combined use of satellite imagery and ma... Mostrar Tudo |
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Palavras-Chave: |
Pecuária de precisão. |
|
Thesagro: |
Biomassa; Pastagem; Sensoriamento Remoto; Tecnologia. |
|
Thesaurus NAL: |
Biomass; Pasture management; Remote sensing; Tropical grasslands. |
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Categoria do assunto: |
-- |
|
Marc: |
LEADER 02758naa a2200313 a 4500
001 2159571
005 2023-12-12
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s11119-023-10013-z$2DOI
100 1 $aBRETAS, I. L.
245 $aCanopy height and biomass prediction in Mombaça guinea grass pastures using satellite imagery and machine learning.$h[electronic resource]
260 $c2023
500 $aPublished online: 17 April 2023.
520 $aABSTRACT - Remote sensing can serve as a promising solution for monitoring spatio-temporal variability in grasslands, providing timely information about diferent biophysical parameters. We aimed to develop models for canopy height classifcation and aboveground biomass estimation in pastures of Megathyrsus maximus cv. Mombaça using machine learning techniques and images obtained from the Sentinel-2 satellite. We used diferent spectral bands from the Sentinel-2, which were obtained and processed entirely in the cloud computing space. Three canopy height classes were defned according to grazing management recommendations: Class 0 (<0.45 m), Class 1 (0.45–0.80 m) and Class 2 (>0.80 m). For modeling, the original database was divided into training data (85%) and test data (15%). To avoid dependency between our training and test datasets and ensure greater generalization capacity, we used a spatial grouping evaluation structure. The random forest algorithm was used to predict canopy height and aboveground biomass by using height and biomass feld reference data obtained from 54 paddocks in Brazil between 2016 and 2018. Our results demonstrated precision, recall, and accuracy values of up to 73%, 73%, and 72%, respectively, for canopy height classifcation. In addition, the models showed reasonable predictive performance for aboveground fresh biomass (AFB) and dry matter concentration (DMC; R2=0.61 and 0.69, respectively). We conclude that the combined use of satellite imagery and machine learning techniques has potential to predict canopy height and aboveground biomass of Megathyrsus maximus cv. Mombaça. However, further studies should be conducted to improve the proposed models and develop software to implement the tool under feld conditions.
650 $aBiomass
650 $aPasture management
650 $aRemote sensing
650 $aTropical grasslands
650 $aBiomassa
650 $aPastagem
650 $aSensoriamento Remoto
650 $aTecnologia
653 $aPecuária de precisão
700 1 $aVALENTE, D. S. M.
700 1 $aOLIVEIRA, T. F. DE
700 1 $aMONTAGNER, D. B.
700 1 $aEUCLIDES, V. P. B.
700 1 $aCHIZZOTTI, F. H. M.
773 $tPrecision Agriculture$gv. 24, n. 4, p. 1638–1662, 2023.
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