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dc.contributor.authorChicchón Apaza, Miguel Ángel
dc.contributor.authorHuerta, Ronny
dc.contributor.otherChicchón Apaza, Miguel Ángeles_PE
dc.identifier.citationChicchón M. & Huerta R. (2021). Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed. In: Lossio-Ventura J.A., Valverde-Rebaza J.C., Díaz E., Alatrista-Salas H. (eds.) Information Management and Big Data: Seventh Annual International Conference, SIMBig 2020, Lima, Perú, October 1–3, 2020, Proceedings, Communications in Computer and Information Science (vol.1410, pp. 236-249). Springer.
dc.descriptionIndexado en Scopuses_PE
dc.description.abstractPeru is one of the main producers of a wide variety of native potatoes in the world. Nevertheless, to achieve a competitive export of derived products is necessary to implement automation tasks in the production process. Nowadays, volume measurements of native potatoes are done manually, increasing production costs. To reduce these costs, a deep approach based on convolutional neural networks have been developed, tested, and evaluated, using a portable machine vision system to improve high-speed native potato volume estimations. The system was tested under different conditions and was able to detect volume with up to 90% of accuracy.en_EN
dc.sourceRepositorio Institucional - Ulimaes_PE
dc.sourceUniversidad de Limaes_PE
dc.subjectPapas (tubérculos)es_PE
dc.subjectProducción eficientees_PE
dc.subjectIndustria alimentariaes_PE
dc.subjectLean manufacturingen_EN
dc.subjectFood industry and tradeen_EN
dc.subject.classificationIngeniería industrial / Producciónes_PE
dc.titleSemantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speeden_EN
dc.type.otherArtículo de conferencia en Scopus
ulima.areas.lineasdeinvestigacionProductividad y empleo / Innovación: tecnologías y productoses_PE
dc.identifier.journalCommunications in Computer and Information Science
dc.description.peer-reviewRevisión por pareses_PE

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