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Registro Completo |
Biblioteca(s): |
Embrapa Semiárido. |
Data corrente: |
11/08/1995 |
Data da última atualização: |
29/05/2015 |
Autoria: |
MEJIA, N.; GEBAUER, M.; MUNOZ, L.; HEWSTONE, N.; MUNOZ, C.; HINRICHSE, P. |
Título: |
Identification of QTLs for seedlessness, Berry Size, and ripening date in a seedless x seedless table grape progeny. |
Ano de publicação: |
2007 |
Fonte/Imprenta: |
American Journal of Enology and Viticulture, v. 58, n. 4, p. 499-507, 2007. |
Idioma: |
Inglês |
Palavras-Chave: |
Amadurecimento; Cultivo; Uva sem semente. |
Thesagro: |
Uva; Vitis Vinifera. |
Thesaurus Nal: |
Grapes. |
Categoria do assunto: |
A Sistemas de Cultivo |
Marc: |
LEADER 00717naa a2200241 a 4500 001 1142778 005 2015-05-29 008 2007 bl uuuu u00u1 u #d 100 1 $aMEJIA, N. 245 $aIdentification of QTLs for seedlessness, Berry Size, and ripening date in a seedless x seedless table grape progeny.$h[electronic resource] 260 $c2007 650 $aGrapes 650 $aUva 650 $aVitis Vinifera 653 $aAmadurecimento 653 $aCultivo 653 $aUva sem semente 700 1 $aGEBAUER, M. 700 1 $aMUNOZ, L. 700 1 $aHEWSTONE, N. 700 1 $aMUNOZ, C. 700 1 $aHINRICHSE, P. 773 $tAmerican Journal of Enology and Viticulture$gv. 58, n. 4, p. 499-507, 2007.
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Embrapa Semiárido (CPATSA) |
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Biblioteca(s): |
Embrapa Acre. |
Data corrente: |
31/07/2020 |
Data da última atualização: |
28/06/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
FERREIRA, M. P.; ALMEIDA, D. R. A. de; PAPA, D. de A.; MINERVINO, J. B. S.; VERAS, H. F. P.; FORMIGHIERI, A.; SANTOS, C. A. N.; FERREIRA, M. A. D.; FIGUEIREDO, E. O.; FERREIRA, E. J. L. |
Afiliação: |
Matheus Pinheiro Ferreira, Instituto Militar de Engenharia (IME); Danilo Roberti Alves de Almeida, Universidade de São Paulo (USP); DANIEL DE ALMEIDA PAPA, CPAF-AC; Juliano Baldez Silva Minervino, Universidade Federal do Acre (Ufac); Hudson Franklin Pessoa Veras, Universidade Federal do Paraná (UFPR); Arthur Formighieri, Universidade Federal do Acre (Ufac); Caio Alexandre Nascimento Santos, Bolsista Embrapa Acre; Marcio Aurélio Dantas Ferreira, Fundação de Tecnologia do Estado do Acre (Funtac); EVANDRO ORFANO FIGUEIREDO, CPAF-AC; Evandro José Linhares Ferreira, Instituto Nacional de Pesquisas da Amazônia (Inpa). |
Título: |
Individual tree detection and species classification of Amazonian palms using UAV images and deep learning. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Forest Ecology and Management, v. 475, n. 118397, p. 1-11, 2020. |
ISSN: |
0378-1127 |
DOI: |
https://doi.org/10.1016/j.foreco.2020.118397 |
Idioma: |
Inglês |
Conteúdo: |
Information regarding the spatial distribution of palm trees in tropical forests is crucial for commercial exploitation and management. However, spatially continuous knowledge of palms occurrence is scarce and difficult to obtain with conventional approaches such as field inventories. Here, we developed a new method to map Amazonian palm species at the individual tree crown (ITC) level using RGB images acquired by a low-cost unmanned aerial vehicle (UAV). Our approach is based on morphological operations performed in the score maps of palm species derived from a fully convolutional neural network model. We first constructed a labeled dataset by dividing the study area (135 ha within an old-growth Amazon forest) into 28 plots of 250 m×150 m. Then, we manually outlined all palm trees seen in RGB images with 4 cm pixels. We identified three palm species: Attalea butyracea, Euterpe precatoria and Iriartea deltoidea. We randomly selected 22 plots (80%) for training and six plots (20%) for testing. We changed the plots for training and testing to evaluate the variabilityn, in the classification accuracy and assess model generalization. Our method outperformed the average producer?s accuracy of conventional patch-wise semantic segmentation (CSS) in 4.7%. Moreover, our method correctly identified, on average, 34.7 percentage points more ITCs than CSS, which tended to merge trees that are close to each other. The producer's accuracy of A. butyracea, E. precatoria and I. deltoidea was 78.6 ± 5.5%, 8.6 ± 1.4% and 96.6 ± 3.4%, respectively. Fortunately, one of the most exploited and commercialized palm species in the Amazon (E. precatoria, a.k.a, Açaí) was mapped with the highest classification accuracy. Maps of E. precatoria derived from low-cost UAV systems can support management projects and community-based forest monitoring programs in the Amazon. MenosInformation regarding the spatial distribution of palm trees in tropical forests is crucial for commercial exploitation and management. However, spatially continuous knowledge of palms occurrence is scarce and difficult to obtain with conventional approaches such as field inventories. Here, we developed a new method to map Amazonian palm species at the individual tree crown (ITC) level using RGB images acquired by a low-cost unmanned aerial vehicle (UAV). Our approach is based on morphological operations performed in the score maps of palm species derived from a fully convolutional neural network model. We first constructed a labeled dataset by dividing the study area (135 ha within an old-growth Amazon forest) into 28 plots of 250 m×150 m. Then, we manually outlined all palm trees seen in RGB images with 4 cm pixels. We identified three palm species: Attalea butyracea, Euterpe precatoria and Iriartea deltoidea. We randomly selected 22 plots (80%) for training and six plots (20%) for testing. We changed the plots for training and testing to evaluate the variabilityn, in the classification accuracy and assess model generalization. Our method outperformed the average producer?s accuracy of conventional patch-wise semantic segmentation (CSS) in 4.7%. Moreover, our method correctly identified, on average, 34.7 percentage points more ITCs than CSS, which tended to merge trees that are close to each other. The producer's accuracy of A. butyracea, E. precatoria and I. deltoidea was... Mostrar Tudo |
Palavras-Chave: |
Acre; Aerial surveys; Amaz; Amazonia Occidental; Amazônia Ocidental; Bosques lluviosos; DeepLabv3+; Drone; Embrapa Acre; Fotografía aérea; Imagem RGB; Madera tropical; Mapeamento; Palm trees; Palmeira; Rio Branco (AC); Teledetección; Vehículos aéreos no tripulados; Western Amazon. |
Thesagro: |
Açaí; Aerofotogrametria; Biogeografia; Espécie Nativa; Floresta Tropical; População de Planta; Sensoriamento Remoto. |
Thesaurus NAL: |
Aerial photography; Arecaceae; Biogeography; Euterpe precatoria; Rain forests; Remote sensing; Tropical wood; Unmanned aerial vehicles. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/215053/1/27014.pdf
|
Marc: |
LEADER 03750naa a2200661 a 4500 001 2124129 005 2021-06-28 008 2020 bl uuuu u00u1 u #d 022 $a0378-1127 024 7 $ahttps://doi.org/10.1016/j.foreco.2020.118397$2DOI 100 1 $aFERREIRA, M. P. 245 $aIndividual tree detection and species classification of Amazonian palms using UAV images and deep learning.$h[electronic resource] 260 $c2020 520 $aInformation regarding the spatial distribution of palm trees in tropical forests is crucial for commercial exploitation and management. However, spatially continuous knowledge of palms occurrence is scarce and difficult to obtain with conventional approaches such as field inventories. Here, we developed a new method to map Amazonian palm species at the individual tree crown (ITC) level using RGB images acquired by a low-cost unmanned aerial vehicle (UAV). Our approach is based on morphological operations performed in the score maps of palm species derived from a fully convolutional neural network model. We first constructed a labeled dataset by dividing the study area (135 ha within an old-growth Amazon forest) into 28 plots of 250 m×150 m. Then, we manually outlined all palm trees seen in RGB images with 4 cm pixels. We identified three palm species: Attalea butyracea, Euterpe precatoria and Iriartea deltoidea. We randomly selected 22 plots (80%) for training and six plots (20%) for testing. We changed the plots for training and testing to evaluate the variabilityn, in the classification accuracy and assess model generalization. Our method outperformed the average producer?s accuracy of conventional patch-wise semantic segmentation (CSS) in 4.7%. Moreover, our method correctly identified, on average, 34.7 percentage points more ITCs than CSS, which tended to merge trees that are close to each other. The producer's accuracy of A. butyracea, E. precatoria and I. deltoidea was 78.6 ± 5.5%, 8.6 ± 1.4% and 96.6 ± 3.4%, respectively. Fortunately, one of the most exploited and commercialized palm species in the Amazon (E. precatoria, a.k.a, Açaí) was mapped with the highest classification accuracy. Maps of E. precatoria derived from low-cost UAV systems can support management projects and community-based forest monitoring programs in the Amazon. 650 $aAerial photography 650 $aArecaceae 650 $aBiogeography 650 $aEuterpe precatoria 650 $aRain forests 650 $aRemote sensing 650 $aTropical wood 650 $aUnmanned aerial vehicles 650 $aAçaí 650 $aAerofotogrametria 650 $aBiogeografia 650 $aEspécie Nativa 650 $aFloresta Tropical 650 $aPopulação de Planta 650 $aSensoriamento Remoto 653 $aAcre 653 $aAerial surveys 653 $aAmaz 653 $aAmazonia Occidental 653 $aAmazônia Ocidental 653 $aBosques lluviosos 653 $aDeepLabv3+ 653 $aDrone 653 $aEmbrapa Acre 653 $aFotografía aérea 653 $aImagem RGB 653 $aMadera tropical 653 $aMapeamento 653 $aPalm trees 653 $aPalmeira 653 $aRio Branco (AC) 653 $aTeledetección 653 $aVehículos aéreos no tripulados 653 $aWestern Amazon 700 1 $aALMEIDA, D. R. A. de 700 1 $aPAPA, D. de A. 700 1 $aMINERVINO, J. B. S. 700 1 $aVERAS, H. F. P. 700 1 $aFORMIGHIERI, A. 700 1 $aSANTOS, C. A. N. 700 1 $aFERREIRA, M. A. D. 700 1 $aFIGUEIREDO, E. O. 700 1 $aFERREIRA, E. J. L. 773 $tForest Ecology and Management$gv. 475, n. 118397, p. 1-11, 2020.
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