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 | Acesso ao texto completo restrito à biblioteca da Embrapa Cerrados. Para informações adicionais entre em contato com cpac.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
03/04/2018 |
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Data da última atualização: |
03/04/2018 |
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Autoria: |
SANCHES, G. M.; DUFT, D. G.; KÖLLN, O. T.; LUCIANO, A. C. dos S.; CASTRO, S. G. Q. de; OKUNO, F. M.; FRANCO, H. C. J. |
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Afiliação: |
Guilherme Martineli Sanches, CNPEM; DANIEL GARBELLINI DUFT, CNPEM; ORIEL TIAGO KÖLLN, CNPEM; ANA CLAUDIA DOS SANTOS LUCIANO, CNPEM; SÉRGIO GUSTAVO QUASSI DE CASTRO, CNPEM; FÁBIO MAKOTO OKUNO, CNPEM; HENRIQUE COUTINHO JUNQUEIRA FRANCO, CNPEM. |
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Título: |
The potential for RGB images obtained using unmanned aerial vehicle to assess and predict yield in sugarcane fields. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
International Journal of Remote Sensing, 13 Mar. 2018. |
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DOI: |
https://doi.org/10.1080/01431161.2018.1448484 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT Estimating yield is a major challenge for the majority of agricultural crops. With the advancement of field technologies however, especially those related to the use of Unmanned Aerial Vehicles (UAV) or Drones, the quality of available information has increased, making it possible to overcome technological bottlenecks. However, drone technologies have advanced much faster than studies dealing with the treatment and analysis of information, which can represent an obstacle to the complete adoption of such technologies in sugarcane fields. The objective of the present study was to evaluate the potential for UAV images to assess the degree of canopy closure from different planting approaches and row-spacing treatments applied to sugarcane crop, in order to assess the potential of these tools to predict crop yield. The vegetative growth of the crop was evaluated and the images were obtained at the point of maximum tillering and the inflection point of the biomass accumulation curve. The evaluations included the index; LAI (Leaf Area Index) and GRVI (Green-Red Vegetation Index) obtained by field sensor and UAV, respectively. Because the images from UAV cover the total area, the results revealed that GRVI appears to be much better able to reflect the whole condition of the crop yield (R2 = 0.69) in the field when compared to LAI (R2 = 0.34); demonstrated convincingly by the high spatial resolution capacity of the technology. When integrated, these two indices were able to improve yield estimates by 10% (R2 = 0.79). Images obtained using UAV can represent a low-cost tool for obtaining high-precision remote data that can be used to estimate the agricultural yield of sugarcane fields; and in this way are an effective tool to aid decision making by growers. MenosABSTRACT Estimating yield is a major challenge for the majority of agricultural crops. With the advancement of field technologies however, especially those related to the use of Unmanned Aerial Vehicles (UAV) or Drones, the quality of available information has increased, making it possible to overcome technological bottlenecks. However, drone technologies have advanced much faster than studies dealing with the treatment and analysis of information, which can represent an obstacle to the complete adoption of such technologies in sugarcane fields. The objective of the present study was to evaluate the potential for UAV images to assess the degree of canopy closure from different planting approaches and row-spacing treatments applied to sugarcane crop, in order to assess the potential of these tools to predict crop yield. The vegetative growth of the crop was evaluated and the images were obtained at the point of maximum tillering and the inflection point of the biomass accumulation curve. The evaluations included the index; LAI (Leaf Area Index) and GRVI (Green-Red Vegetation Index) obtained by field sensor and UAV, respectively. Because the images from UAV cover the total area, the results revealed that GRVI appears to be much better able to reflect the whole condition of the crop yield (R2 = 0.69) in the field when compared to LAI (R2 = 0.34); demonstrated convincingly by the high spatial resolution capacity of the technology. When integrated, these two indices were able to ... Mostrar Tudo |
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Palavras-Chave: |
Imagem Digital; Veículo Aéreo Não Tripulado. |
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Thesagro: |
Aerofotogrametria; Aviação Agrícola; Cana de açúcar; Reconhecimento Aéreo; Sistema de Informação Geográfica. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02735naa a2200289 a 4500
001 2090186
005 2018-04-03
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1080/01431161.2018.1448484$2DOI
100 1 $aSANCHES, G. M.
245 $aThe potential for RGB images obtained using unmanned aerial vehicle to assess and predict yield in sugarcane fields.$h[electronic resource]
260 $c2018
520 $aABSTRACT Estimating yield is a major challenge for the majority of agricultural crops. With the advancement of field technologies however, especially those related to the use of Unmanned Aerial Vehicles (UAV) or Drones, the quality of available information has increased, making it possible to overcome technological bottlenecks. However, drone technologies have advanced much faster than studies dealing with the treatment and analysis of information, which can represent an obstacle to the complete adoption of such technologies in sugarcane fields. The objective of the present study was to evaluate the potential for UAV images to assess the degree of canopy closure from different planting approaches and row-spacing treatments applied to sugarcane crop, in order to assess the potential of these tools to predict crop yield. The vegetative growth of the crop was evaluated and the images were obtained at the point of maximum tillering and the inflection point of the biomass accumulation curve. The evaluations included the index; LAI (Leaf Area Index) and GRVI (Green-Red Vegetation Index) obtained by field sensor and UAV, respectively. Because the images from UAV cover the total area, the results revealed that GRVI appears to be much better able to reflect the whole condition of the crop yield (R2 = 0.69) in the field when compared to LAI (R2 = 0.34); demonstrated convincingly by the high spatial resolution capacity of the technology. When integrated, these two indices were able to improve yield estimates by 10% (R2 = 0.79). Images obtained using UAV can represent a low-cost tool for obtaining high-precision remote data that can be used to estimate the agricultural yield of sugarcane fields; and in this way are an effective tool to aid decision making by growers.
650 $aAerofotogrametria
650 $aAviação Agrícola
650 $aCana de açúcar
650 $aReconhecimento Aéreo
650 $aSistema de Informação Geográfica
653 $aImagem Digital
653 $aVeículo Aéreo Não Tripulado
700 1 $aDUFT, D. G.
700 1 $aKÖLLN, O. T.
700 1 $aLUCIANO, A. C. dos S.
700 1 $aCASTRO, S. G. Q. de
700 1 $aOKUNO, F. M.
700 1 $aFRANCO, H. C. J.
773 $tInternational Journal of Remote Sensing, 13 Mar. 2018.
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| 36. |  | CORLETA, A.; MOURÃO JUNIOR, M.; BARBOSA, R. I. Padrão espacial de regeneração sob copa de caimbé (Curatella americana L., Dileniacea), uso de envoltório Alfa. In. REUNIÃO ANUAL DA REGIÃO BRASILEIRA DA SOCIEDADE INTERNACIONAL DE BIOMETRIA, 52.; SIMPÓSIO DE ESTATÍSTICA APLICADA À EXPERIMENTAÇÃO AGRONÔMICA, 12., Santa Maria, RS, 2007.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
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