Ruiz Zamarreño, CarlosGracia Moisés, AnderVitoria Pascual, IgnacioImas González, José JavierCastaño de Egüés, Lorena YvethAvedillo de la Casa, AmaiaMatías Maestro, Ignacio2023-05-162023-05-162022Zamarreno, C. R., Gracia-Moises, A., Vitoria, I., Imas, J. J., Castano, L., Avedillo, A., & Matias, I. R. (2022). Alfalfa quality detection by means of VIS-NIR optical fiber reflection spectroscopy. 2022 IEEE Sensors, 1-4. https://doi.org/10.1109/SENSORS52175.2022.9967337978-1-6654-8464-010.1109/SENSORS52175.2022.9967337https://academica-e.unavarra.es/handle/2454/45270A first approach study for the classification of alfalfa (medicago sativa) quality has been performed by means of VIS-NIR optical fiber reflection spectroscopy. Reflection spectral data has been obtained from alfalfa samples comprising six different qualities. Obtained data has been classified and organized to feed supervised self-learning algorithms. Neural networks have been used in order to differentiate the quality level of the samples. Obtained results permit to validate the proposed approach with 72% of the samples properly classified. In addition, proposed solution was implemented in a low cost automated detection prototype suitable to be used by non-qualified operators. Obtained equipment consist of a first step towards its utilization in quality monitoring and classification of many other products in the agri-food field.application/pdfeng© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.AlfalfaNeural networksOptical fiberOptical spectroscopyReflectioninfo:eu-repo/semantics/conferenceObject2023-05-16info:eu-repo/semantics/openAccess