Data augmentation techniques for machine learning applied to optical spectroscopy datasets in agrifood applications: a comprehensive review
| dc.contributor.author | Gracia Moisés, Ander | |
| dc.contributor.author | Vitoria Pascual, Ignacio | |
| dc.contributor.author | Imas González, José Javier | |
| dc.contributor.author | Ruiz Zamarreño, Carlos | |
| dc.contributor.department | Ingeniería Eléctrica, Electrónica y de Comunicación | es_ES |
| dc.contributor.department | Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.date.accessioned | 2024-05-21T17:02:55Z | |
| dc.date.available | 2024-05-21T17:02:55Z | |
| dc.date.issued | 2023 | |
| dc.date.updated | 2024-05-21T16:28:31Z | |
| dc.description.abstract | Machine learning (ML) and deep learning (DL) have achieved great success in different tasks. These include computer vision, image segmentation, natural language processing, predicting classification, evaluating time series, and predicting values based on a series of variables. As artificial intelligence progresses, new techniques are being applied to areas like optical spectroscopy and its uses in specific fields, such as the agrifood industry. The performance of ML and DL techniques generally improves with the amount of data available. However, it is not always possible to obtain all the necessary data for creating a robust dataset. In the particular case of agrifood applications, dataset collection is generally constrained to specific periods. Weather conditions can also reduce the possibility to cover the entire range of classifications with the consequent generation of imbalanced datasets. To address this issue, data augmentation (DA) techniques are employed to expand the dataset by adding slightly modified copies of existing data. This leads to a dataset that includes values from laboratory tests, as well as a collection of synthetic data based on the real data. This review work will present the application of DA techniques to optical spectroscopy datasets obtained from real agrifood industry applications. The reviewed methods will describe the use of simple DA techniques, such as duplicating samples with slight changes, as well as the utilization of more complex algorithms based on deep learning generative adversarial networks (GANs), and semi-supervised generative adversarial networks (SGANs). | en |
| dc.description.sponsorship | This work was supported in part by the Industrial Doctorate grants 2021 from Government and by the PID2022-1374370B-100 research grant from the Ministry of Science and Innovation | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Gracia Moisés, A., Vitoria Pascual, I., Imas González, J. J., Zamarreño, C. R. (2023) Data augmentation techniques for machine learning applied to optical spectroscopy datasets in agrifood applications: A comprehensive review. Sensors, 23(20), 1-29. https://doi.org/10.3390/s23208562. | en |
| dc.identifier.doi | 10.3390/s23208562 | |
| dc.identifier.issn | 1424-8220 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/48147 | |
| dc.language.iso | eng | en |
| dc.publisher | MDPI | en |
| dc.relation.ispartof | Sensors 2023, 23(20), 8562 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI//PID2022-1374370B-100/ | |
| dc.relation.publisherversion | https://doi.org/10.3390/s23208562 | |
| dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Optical spectroscopy | en |
| dc.subject | Agrifood industry | en |
| dc.subject | Artificial intelligence | en |
| dc.subject | Data augmentation (DA) | en |
| dc.subject | Generative adversarial networks (GANs) | en |
| dc.title | Data augmentation techniques for machine learning applied to optical spectroscopy datasets in agrifood applications: a comprehensive review | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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