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| 3. |  | SILVA, E. Avaliação de impacto ambiental de um plano de manejo florestal de rendimento sustentável, Matias Barbosa, MG. In: INTERNATIONAL CONGRESS AND EXHIBITION ON FOREST, 5., 1999, Curitiba. Forest 99: [resumos]. Rio de Janeiro: BIOSFERA, 1999. 1 CD-ROM. Autoria bilíngue: CONGRESSO E EXPOSICAO INTERNACIONAL SOBRE FLORESTAS, 5., 1999, Curitiba.| Biblioteca(s): Embrapa Florestas. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agroindústria Tropical. Para informações adicionais entre em contato com cnpat.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agroindústria Tropical. |
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Data corrente: |
17/01/2020 |
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Data da última atualização: |
23/01/2024 |
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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: |
CALIXTO, R. R.; NETO, L. G. P.; CAVALCANTE, T. DA S.; ARAGÃO, M. F.; SILVA, E. de O. |
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Afiliação: |
RENÊ RIPARDO CALIXTO, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Campus Sobral, CE, Brazil; LUIS GONZAGA PINHEIRO NETO, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Campus Sobral, CE, Brazil; TARIQUE DA SILVEIRA CAVALCANTE, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Campus Sobral, CE, Brazil; MÁRCIO FACUNDO ARAGÃO, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Campus Sobral, CE, Brazil; EBENEZER DE OLIVEIRA SILVA, CNPAT. |
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Título: |
A computer vision model development for size and weight estimation of yellow melon in the Brazilian Northeast. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Scientia Horticulturae, New York, v. 256, artigo 108521, 8 p., 15 Oct. 2019. |
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DOI: |
https://doi.org/10.1016/j.scienta.2019.05.048 |
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Idioma: |
Inglês |
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Conteúdo: |
This work aimed to predict mass (weight) and shape ratio (format) of yellow melon through computer vision techniques (VC). To do this, a digital camera was used to take pictures of all melons (n=135). The images processing consisted in filtering colors in the RGB space, thresholding by Otsu's method and, finally, detection of melon's contours. The used processing techniques were sufficient to separate the melon from the image background, allowing calculating the area of the melon (Amelon), in both square pixel (pixel2) or square centimeters (cm2), which showed very strong Pearson’s correlation (0.993**). By using area-based linear regressions, it was possible to predict the weight of the melon in kilograms, from Amelon in pixel2 (Pearson’s correlation = 0.993**) or cm2 (0.989**). The shape ratio (SR) has estimated based on melon’s diameters (L - longitudinal and T - transversal), which were obtained using pachymeter (real) or computer vision (CV). Based on real data set (pachymeter), melons were classified by SR into four groups, considered as reference. The based-CV algorithm was able to classify the same melons in the same groups with hit percentage of 96%. The correlation between the product of the multiplication of both diameters (Lcv*Tcv) and the melon area (Amelon, pixel2) was very strong (0.9987**), and the coefficient of calibration of 93 pixels per centimeter presented a good fit. Based on these two results, the weight, in kilograms, and both diameters, in centimeters, were predicted from the measurements of L and T, in pixel, obtained by computer vision. MenosThis work aimed to predict mass (weight) and shape ratio (format) of yellow melon through computer vision techniques (VC). To do this, a digital camera was used to take pictures of all melons (n=135). The images processing consisted in filtering colors in the RGB space, thresholding by Otsu's method and, finally, detection of melon's contours. The used processing techniques were sufficient to separate the melon from the image background, allowing calculating the area of the melon (Amelon), in both square pixel (pixel2) or square centimeters (cm2), which showed very strong Pearson’s correlation (0.993**). By using area-based linear regressions, it was possible to predict the weight of the melon in kilograms, from Amelon in pixel2 (Pearson’s correlation = 0.993**) or cm2 (0.989**). The shape ratio (SR) has estimated based on melon’s diameters (L - longitudinal and T - transversal), which were obtained using pachymeter (real) or computer vision (CV). Based on real data set (pachymeter), melons were classified by SR into four groups, considered as reference. The based-CV algorithm was able to classify the same melons in the same groups with hit percentage of 96%. The correlation between the product of the multiplication of both diameters (Lcv*Tcv) and the melon area (Amelon, pixel2) was very strong (0.9987**), and the coefficient of calibration of 93 pixels per centimeter presented a good fit. Based on these two results, the weight, in kilograms, and both diameters, in centimete... Mostrar Tudo |
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Palavras-Chave: |
Cucumis melo L; Digital image processing; Yellow melon. |
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Thesagro: |
Cucumis Melo; Exportação; Melão. |
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Thesaurus NAL: |
Computer vision. |
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Categoria do assunto: |
Q Alimentos e Nutrição Humana |
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Marc: |
LEADER 02442naa a2200265 a 4500
001 2118935
005 2024-01-23
008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.scienta.2019.05.048$2DOI
100 1 $aCALIXTO, R. R.
245 $aA computer vision model development for size and weight estimation of yellow melon in the Brazilian Northeast.$h[electronic resource]
260 $c2019
520 $aThis work aimed to predict mass (weight) and shape ratio (format) of yellow melon through computer vision techniques (VC). To do this, a digital camera was used to take pictures of all melons (n=135). The images processing consisted in filtering colors in the RGB space, thresholding by Otsu's method and, finally, detection of melon's contours. The used processing techniques were sufficient to separate the melon from the image background, allowing calculating the area of the melon (Amelon), in both square pixel (pixel2) or square centimeters (cm2), which showed very strong Pearson’s correlation (0.993**). By using area-based linear regressions, it was possible to predict the weight of the melon in kilograms, from Amelon in pixel2 (Pearson’s correlation = 0.993**) or cm2 (0.989**). The shape ratio (SR) has estimated based on melon’s diameters (L - longitudinal and T - transversal), which were obtained using pachymeter (real) or computer vision (CV). Based on real data set (pachymeter), melons were classified by SR into four groups, considered as reference. The based-CV algorithm was able to classify the same melons in the same groups with hit percentage of 96%. The correlation between the product of the multiplication of both diameters (Lcv*Tcv) and the melon area (Amelon, pixel2) was very strong (0.9987**), and the coefficient of calibration of 93 pixels per centimeter presented a good fit. Based on these two results, the weight, in kilograms, and both diameters, in centimeters, were predicted from the measurements of L and T, in pixel, obtained by computer vision.
650 $aComputer vision
650 $aCucumis Melo
650 $aExportação
650 $aMelão
653 $aCucumis melo L
653 $aDigital image processing
653 $aYellow melon
700 1 $aNETO, L. G. P.
700 1 $aCAVALCANTE, T. DA S.
700 1 $aARAGÃO, M. F.
700 1 $aSILVA, E. de O.
773 $tScientia Horticulturae, New York$gv. 256, artigo 108521, 8 p., 15 Oct. 2019.
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