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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
12/07/2024 |
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Data da última atualização: |
12/07/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MICHELON, T. B.; CARSTENSEN, J. M.; VIEIRA, E. S. N.; PANOBIANCO, M. |
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Afiliação: |
THOMAS BRUNO MICHELON, UNIVERSIDADE FEDERAL DO PARANÁ; JENS MICHAEL CARSTENSEN, TECHNICAL UNIVERSITY OF DENMARK; ELISA SERRA NEGRA VIEIRA, CNPF; MARISTELA PANOBIANCO, UNIVERSIDADE FEDERAL DO PARANÁ. |
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Título: |
Multispectral imaging for distinguishing hybrid forest seeds of Corymbia spp. and Eucalyptus spp. from their progenitors. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Journal of Environmental Management, v. 363, 121383, 2024. |
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ISSN: |
0301-4797 |
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DOI: |
10.1016/j.jenvman.2024.121383 |
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Idioma: |
Inglês |
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Conteúdo: |
In the forest industry, interspecific hybridization, such as Eucalyptus urograndis (Eucalyptus grandis × Eucalyptus urophylla) and Corymbia maculata × Corymbia torelliana, has led to the development of high-performing F1 generations. The successful breeding of these hybrids relies on verifying progenitor origins and confirming post-crossing, but conventional genotype identification methods are resource-intensive and result in seed destruction. As an alternative, multispectral imaging analysis has emerged as an efficient and non-destructive tool for seed phenotyping. This approach has demonstrated success in various crop seeds. However, identifying seed species in the context of forest seeds presents unique challenges due to their natural phenotypic variability and the striking resemblance between different species. This study evaluates the efficacy of spectral imaging analysis in distinguishing hybrid seeds of E. urograndis and C. maculata × C. torelliana from their progenitors. Four experiments were conducted: one for Corymbia spp. seeds, one for each Eucalyptus spp. batch separately, and one for pooled batches. Multispectral images were acquired at 19 wavelengths within the spectral range of 365–970 nm. Classification models based on Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) was created using reflectance and reflectance features, combined with color, shape, and texture features, as well as nCDA transformed features. The LDA algorithm, combining all features, provided the highest accuracy, reaching 98.15% for Corymbia spp., and 92.75%, 85.38, and 86.00 for Eucalyptus batch one, two, and pooled batches, respectively. The study demonstrated the effectiveness of multispectral imaging in distinguishing hybrid seeds of Eucalyptus and Corymbia species. The seeds’ spectral signature played a key role in this differentiation. This technology holds great potential for non-invasively classifying forest seeds in breeding programs. MenosIn the forest industry, interspecific hybridization, such as Eucalyptus urograndis (Eucalyptus grandis × Eucalyptus urophylla) and Corymbia maculata × Corymbia torelliana, has led to the development of high-performing F1 generations. The successful breeding of these hybrids relies on verifying progenitor origins and confirming post-crossing, but conventional genotype identification methods are resource-intensive and result in seed destruction. As an alternative, multispectral imaging analysis has emerged as an efficient and non-destructive tool for seed phenotyping. This approach has demonstrated success in various crop seeds. However, identifying seed species in the context of forest seeds presents unique challenges due to their natural phenotypic variability and the striking resemblance between different species. This study evaluates the efficacy of spectral imaging analysis in distinguishing hybrid seeds of E. urograndis and C. maculata × C. torelliana from their progenitors. Four experiments were conducted: one for Corymbia spp. seeds, one for each Eucalyptus spp. batch separately, and one for pooled batches. Multispectral images were acquired at 19 wavelengths within the spectral range of 365–970 nm. Classification models based on Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) was created using reflectance and reflectance features, combined with color, shape, and texture features, as well as nCDA transformed features. The LDA al... Mostrar Tudo |
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Palavras-Chave: |
Aprendizado de máquina; Imagem espectral; Inteligência artificial; Machine learning; Machine vision; Phenotyping; Spectral imaging; Visão de máquina. |
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Thesagro: |
Eucalyptus spp; Fenótipo; Reprodução Vegetal; Semente Florestal. |
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Thesaurus Nal: |
Breeding; Corymbia. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
LEADER 03063naa a2200349 a 4500
001 2165764
005 2024-07-12
008 2024 bl uuuu u00u1 u #d
022 $a0301-4797
024 7 $a10.1016/j.jenvman.2024.121383$2DOI
100 1 $aMICHELON, T. B.
245 $aMultispectral imaging for distinguishing hybrid forest seeds of Corymbia spp. and Eucalyptus spp. from their progenitors.$h[electronic resource]
260 $c2024
520 $aIn the forest industry, interspecific hybridization, such as Eucalyptus urograndis (Eucalyptus grandis × Eucalyptus urophylla) and Corymbia maculata × Corymbia torelliana, has led to the development of high-performing F1 generations. The successful breeding of these hybrids relies on verifying progenitor origins and confirming post-crossing, but conventional genotype identification methods are resource-intensive and result in seed destruction. As an alternative, multispectral imaging analysis has emerged as an efficient and non-destructive tool for seed phenotyping. This approach has demonstrated success in various crop seeds. However, identifying seed species in the context of forest seeds presents unique challenges due to their natural phenotypic variability and the striking resemblance between different species. This study evaluates the efficacy of spectral imaging analysis in distinguishing hybrid seeds of E. urograndis and C. maculata × C. torelliana from their progenitors. Four experiments were conducted: one for Corymbia spp. seeds, one for each Eucalyptus spp. batch separately, and one for pooled batches. Multispectral images were acquired at 19 wavelengths within the spectral range of 365–970 nm. Classification models based on Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) was created using reflectance and reflectance features, combined with color, shape, and texture features, as well as nCDA transformed features. The LDA algorithm, combining all features, provided the highest accuracy, reaching 98.15% for Corymbia spp., and 92.75%, 85.38, and 86.00 for Eucalyptus batch one, two, and pooled batches, respectively. The study demonstrated the effectiveness of multispectral imaging in distinguishing hybrid seeds of Eucalyptus and Corymbia species. The seeds’ spectral signature played a key role in this differentiation. This technology holds great potential for non-invasively classifying forest seeds in breeding programs.
650 $aBreeding
650 $aCorymbia
650 $aEucalyptus spp
650 $aFenótipo
650 $aReprodução Vegetal
650 $aSemente Florestal
653 $aAprendizado de máquina
653 $aImagem espectral
653 $aInteligência artificial
653 $aMachine learning
653 $aMachine vision
653 $aPhenotyping
653 $aSpectral imaging
653 $aVisão de máquina
700 1 $aCARSTENSEN, J. M.
700 1 $aVIEIRA, E. S. N.
700 1 $aPANOBIANCO, M.
773 $tJournal of Environmental Management$gv. 363, 121383, 2024.
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Registro original: |
Embrapa Florestas (CNPF) |
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