Image processing based modeling for Rosa roxburghii fruits mass and volume estimation.
Rosa roxburghii
Estimated modeling
Grading
Image measurement
Physical characteristic
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
05 Jul 2024
05 Jul 2024
Historique:
received:
15
02
2024
accepted:
19
06
2024
medline:
6
7
2024
pubmed:
6
7
2024
entrez:
5
7
2024
Statut:
epublish
Résumé
The mass and volume of Rosa roxburghii fruits are essential for fruit grading and consumer selection. Physical characteristics such as dimension, projected area, mass, and volume are interrelated. Image-based mass and volume estimation facilitates the automation of fruit grading, which can replace time-consuming and laborious manual grading. In this study, image processing techniques were used to extract fruit dimensions and projected areas, and univariate (linear, quadratic, exponential, and power) and multivariate regression models were used to estimate the mass and volume of Rosa roxburghii fruits. The results showed that the quadratic model based on the criterion projected area (CPA) estimated the best mass (R
Identifiants
pubmed: 38969713
doi: 10.1038/s41598-024-65321-9
pii: 10.1038/s41598-024-65321-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
15507Subventions
Organisme : Major Science and Technology Projects of Guizhou
ID : [2019]3003
Organisme : Natural Science Foundation of Guizhou Province
ID : [2019]1233
Informations de copyright
© 2024. The Author(s).
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