Prediction of the ripening stages of papayas using discriminant analysis and support vector machine algorithms.
machine learning algorithms
machine vision system
mechanical properties
papayas
physicochemical properties
ripening stages
Journal
Journal of the science of food and agriculture
ISSN: 1097-0010
Titre abrégé: J Sci Food Agric
Pays: England
ID NLM: 0376334
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
revised:
06
10
2021
received:
05
06
2021
accepted:
20
11
2021
pubmed:
22
11
2021
medline:
24
5
2022
entrez:
21
11
2021
Statut:
ppublish
Résumé
Evaluation of the quality properties of papaya becomes essential due to the acceleration of the fruit shelf-life senescence and the deterioration factor of the expected postharvest operations. In this study, the colour features in RGB, normalised RGB, HSV and L*a*b* channels were extracted and correlated with mechanical properties, moisture content (MC), total soluble solids (TSS) and pH for the prediction of quality properties at five ripening stages of papaya (R1-R5). The mean values of colour features in RGB The study has shown the versatility of a machine vision system in predicting the quality changes in papaya. The results showed that the machine vision system can be used to predict the ripening stages as well as classifying the fruits into different ripening stages of papayas. © 2021 Society of Chemical Industry.
Sections du résumé
BACKGROUND
BACKGROUND
Evaluation of the quality properties of papaya becomes essential due to the acceleration of the fruit shelf-life senescence and the deterioration factor of the expected postharvest operations. In this study, the colour features in RGB, normalised RGB, HSV and L*a*b* channels were extracted and correlated with mechanical properties, moisture content (MC), total soluble solids (TSS) and pH for the prediction of quality properties at five ripening stages of papaya (R1-R5).
RESULTS
RESULTS
The mean values of colour features in RGB
CONCLUSION
CONCLUSIONS
The study has shown the versatility of a machine vision system in predicting the quality changes in papaya. The results showed that the machine vision system can be used to predict the ripening stages as well as classifying the fruits into different ripening stages of papayas. © 2021 Society of Chemical Industry.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3266-3276Informations de copyright
© 2021 Society of Chemical Industry.
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