Application of computer vision systems for assessing bergamot fruit external features
Bergamot Citrus x bergamia Risso & Poiteau is an emblematic Citrus species of Reggio Calabria province (Southern Italy) where more than 90% of the global production thrives. The present work deals with the use of a non-destructive technique based on a computer vision systems to evaluate bergamo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Firenze University Press
2023-05-01
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| Series: | Advances in Horticultural Science |
| Subjects: | |
| Online Access: | https://oaj.fupress.net/index.php/ahs/article/view/13911 |
| Summary: | Bergamot Citrus x bergamia Risso & Poiteau is an emblematic Citrus species of Reggio Calabria province (Southern Italy) where more than 90% of the global production thrives. The present work deals with the use of a non-destructive technique based on a computer vision systems to evaluate bergamot fruit peel colour, as well as dimensional features. To this purpose, experimental trials considered three bergamot cultivars, namely: ‘Femminello’, ‘Castagnaro’ and ‘Fantastico’. Bergamot fruit RGB images were taken using a laboratory inspection chamber equipped with a lighting system and a digital camera Nikon D5200 directly connected to a personal computer, to enable remote image acquisition. First, images were pre-processed according to a previously created colour profile. After that, bergamot fruit colour was analysed and expressed in terms of Hunter L, a, and b coordinates, which were used to calculate Standard Citrus Colour Index (CCI). In addition, dimensional features and shape descriptors were measured for each cultivar. Statistical data analysis, by applying the Kruskal-Wallis test at p<0.05 on CCI data highlighted significant differences between the assessed cultivars, and discriminant analysis (LDA) applied on CCI and dimensional features enabled a classification rate of 78.86% between cultivars, proving the reliability of computer vision techniques in assessing bergamot external features..
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| ISSN: | 0394-6169 1592-1573 |