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Fruit-scan: system to automatically detect raspberry quality using computer vision techniques
| dc.contributor.author | Mora, Marco | |
| dc.date.accessioned | 2023-03-08T13:35:40Z | |
| dc.date.available | 2023-03-08T13:35:40Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/4495 | |
| dc.description.abstract | Chile 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. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Curicó, Chile, i-xxxiv | es_CL |
| dc.subject | Pattern recognition | es_CL |
| dc.subject | Electrical engineering | es_CL |
| dc.subject | Computer vision | es_CL |
| dc.subject | Computer science | es_CL |
| dc.subject | Biometrics (access control) | es_CL |
| dc.subject | Technological innovation | es_CL |
| dc.subject | Quality control | es_CL |
| dc.title | Fruit-scan: system to automatically detect raspberry quality using computer vision techniques | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias de la Ingeniería | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.uri | ieeexplore.ieee.org/document/10006110 | es_CL |
| dc.ucm.doi | doi.org/10.1109/ICA-ACCA56767.2022.10006110 | es_CL |
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