@inproceedings{de9e8f2cf2df42288f373f322cc950b5,
title = "Alfalfa quality detection by means of VIS-NIR optical fiber reflection spectroscopy",
abstract = "A 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.",
keywords = "alfalfa, neural networks, optical fiber, optical spectroscopy, reflection",
author = "Zamarreno, \{C. R.\} and A. Gracia-Moises and I. Vitoria and Imas, \{J. J.\} and L. Castano and A. Avedillo and Matias, \{Ignacio R.\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Sensors Conference, SENSORS 2022 ; Conference date: 30-10-2022 Through 02-11-2022",
year = "2022",
doi = "10.1109/SENSORS52175.2022.9967337",
language = "English",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings",
address = "United States",
}