ESTIMATION OF TOMATO FRUIT FIRMNESS USING DIGITAL IMAGING
ABSTRACT omputer vision systems have proven to be a promising alternative for assessing fruit quality attributes in a non-invasive, instantaneous, and accurate manner. This study aimed to use colorimetric characteristics extracted from digital images to estimate tomato fruit firmness through multiva...
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| Format: | Article |
| Language: | English |
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Sociedade Brasileira de Engenharia Agrícola
2025-08-01
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| Series: | Engenharia Agrícola |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162025000100318&lng=en&tlng=en |
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| _version_ | 1849771690722066432 |
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| author | Anderson G. Costa Layana A. da Silva João C. L. de Carvalho Túlio de A. Machado |
| author_facet | Anderson G. Costa Layana A. da Silva João C. L. de Carvalho Túlio de A. Machado |
| author_sort | Anderson G. Costa |
| collection | DOAJ |
| description | ABSTRACT omputer vision systems have proven to be a promising alternative for assessing fruit quality attributes in a non-invasive, instantaneous, and accurate manner. This study aimed to use colorimetric characteristics extracted from digital images to estimate tomato fruit firmness through multivariate modeling. Images of 80 tomato fruits at four ripening stages were acquired using two digital cameras, enabling the extraction of average intensity values for the red, green, blue, and near-infrared bands, followed by the calculation of colorimetric indices. Reference firmness values were measured using a digital fruit penetrometer. Colorimetric indices were employed to estimate fruit firmness using principal component regression. Principal component analysis enabled the dimensionality to be reduced to a single principal component (explanatory percentage of the data variance of 97.06%), which was used to generate firmness estimation equations. The application of the model to the validation dataset yielded an R2 = 0.937 and a mean standard error (SE) of 2.05 N, demonstrating that the protocol based on colorimetric characteristics extracted from digital images is suitable for estimating tomato firmness. |
| format | Article |
| id | doaj-art-2d5981f1735e45d4aa179e00fc8e17e2 |
| institution | DOAJ |
| issn | 0100-6916 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Sociedade Brasileira de Engenharia Agrícola |
| record_format | Article |
| series | Engenharia Agrícola |
| spelling | doaj-art-2d5981f1735e45d4aa179e00fc8e17e22025-08-20T03:02:32ZengSociedade Brasileira de Engenharia AgrícolaEngenharia Agrícola0100-69162025-08-014510.1590/1809-4430-eng.agric.v45e20250003/2025ESTIMATION OF TOMATO FRUIT FIRMNESS USING DIGITAL IMAGINGAnderson G. Costahttps://orcid.org/0000-0003-0594-8514Layana A. da SilvaJoão C. L. de CarvalhoTúlio de A. MachadoABSTRACT omputer vision systems have proven to be a promising alternative for assessing fruit quality attributes in a non-invasive, instantaneous, and accurate manner. This study aimed to use colorimetric characteristics extracted from digital images to estimate tomato fruit firmness through multivariate modeling. Images of 80 tomato fruits at four ripening stages were acquired using two digital cameras, enabling the extraction of average intensity values for the red, green, blue, and near-infrared bands, followed by the calculation of colorimetric indices. Reference firmness values were measured using a digital fruit penetrometer. Colorimetric indices were employed to estimate fruit firmness using principal component regression. Principal component analysis enabled the dimensionality to be reduced to a single principal component (explanatory percentage of the data variance of 97.06%), which was used to generate firmness estimation equations. The application of the model to the validation dataset yielded an R2 = 0.937 and a mean standard error (SE) of 2.05 N, demonstrating that the protocol based on colorimetric characteristics extracted from digital images is suitable for estimating tomato firmness.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162025000100318&lng=en&tlng=encomputer visioncolorimetric indicestomato productionmultivariate analysis |
| spellingShingle | Anderson G. Costa Layana A. da Silva João C. L. de Carvalho Túlio de A. Machado ESTIMATION OF TOMATO FRUIT FIRMNESS USING DIGITAL IMAGING Engenharia Agrícola computer vision colorimetric indices tomato production multivariate analysis |
| title | ESTIMATION OF TOMATO FRUIT FIRMNESS USING DIGITAL IMAGING |
| title_full | ESTIMATION OF TOMATO FRUIT FIRMNESS USING DIGITAL IMAGING |
| title_fullStr | ESTIMATION OF TOMATO FRUIT FIRMNESS USING DIGITAL IMAGING |
| title_full_unstemmed | ESTIMATION OF TOMATO FRUIT FIRMNESS USING DIGITAL IMAGING |
| title_short | ESTIMATION OF TOMATO FRUIT FIRMNESS USING DIGITAL IMAGING |
| title_sort | estimation of tomato fruit firmness using digital imaging |
| topic | computer vision colorimetric indices tomato production multivariate analysis |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162025000100318&lng=en&tlng=en |
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