Fruit-scan: system to automatically detect raspberry quality using computer vision techniques

dc.creatorMora, Marco
dc.date2023-03-08T13:35:40Z
dc.date2023-03-08T13:35:40Z
dc.date2022
dc.date.accessioned2024-05-02T20:30:39Z
dc.date.available2024-05-02T20:30:39Z
dc.identifierhttp://repositorio.ucm.cl/handle/ucm/4495
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9274739
dc.descriptionChile ranks tenth among the countries that export raspberries. In the Maule Region there are approximately 1,200 families who obtain their economic livelihood based on raspberry production. Raspberry exporting companies carry out quality control of the fruit through a human expert. The quality test consists of visually analyzing a small sample of fruit and determining the percentages of healthy fruit and fruit with defects. This talk presents the results of the Fruit-Scan project: System to automatically detect raspberry quality using computer vision techniques. The developed technology uses convolutional neural networks to analyze images of raspberry trays and count healthy and defective raspberries. The research was financed by the Innovation Fund for Competitiveness FIC of the Regional Government of Maule through the Transfer Project for the Development of the Raspberry Quality Estimation Equipment code 40.001.110-0. © 2022 IEEE.
dc.languageen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceInternational Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Curicó, Chile, i-xxxiv
dc.subjectPattern recognition
dc.subjectElectrical engineering
dc.subjectComputer vision
dc.subjectComputer science
dc.subjectBiometrics (access control)
dc.subjectTechnological innovation
dc.subjectQuality control
dc.titleFruit-scan: system to automatically detect raspberry quality using computer vision techniques
dc.typeArticle


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