Onfield estimation of quality parameters in alfalfa through hyperspectral spectrometer data

Gámez Guzmán, Angie Lorena; Vatter, Thomas; Santesteban García, Gonzaga; Araus, José Luis; Aranjuelo Michelena, Iker

Publication:
Onfield estimation of quality parameters in alfalfa through hyperspectral spectrometer data

dc.contributor.author	Gámez Guzmán, Angie Lorena
dc.contributor.author	Vatter, Thomas
dc.contributor.author	Santesteban García, Gonzaga
dc.contributor.author	Araus, José Luis
dc.contributor.author	Aranjuelo Michelena, Iker
dc.contributor.department	Agronomía, Biotecnología y Alimentación	es_ES
dc.contributor.department	Agronomia, Bioteknologia eta Elikadura	eu
dc.date.accessioned	2024-04-18T18:12:10Z
dc.date.available	2024-04-18T18:12:10Z
dc.date.issued	2024
dc.date.updated	2024-04-18T17:48:13Z
dc.description.abstract	Alfalfa is a forage of vast importance around the world. In the past, near-infrared spectroscopy (NIRS) technique have been explored in the lab to determine quality traits such as fibre content in dried and ground material. During the last decade, portable hyperspectral devices have emerged as a tools for in-field prediction, of not only crop yield but also a large range of quality and physiological markers. The objective of this study was to estimate quality parameters in an alfalfa crop using hyperspectral data acquired from a full-range (350–2500 nm) spectrometer under field conditions. Reflected spectra were measured in single leaves as well as at the canopy level, then reflectance was related to target parameters such as biomass, leaf pigments, sugars, protein, and mineral contents. Due to their large effect on crop quality parameters, meteorological conditions and phenological stages were included as predictors in the models. We found that meteorological and phenological variables improved the accuracies and percentage of variance explained (R2) for most of the parameters evaluated. Based on R2 values, the best prediction models were obtained for biomass (0.71), sucrose (0.65), flavonoids (Flav) (0.56) and nitrogen (0.70) with normalized root mean squared errors of 0.196, 0.32, 0.087 and 0.08, respectively. These parameters were associated mainly with visible (VIS) (approx. 350–700 nm) and near infrared (NIR) (700–1250 nm) regions of the spectrum. Regarding mineral composition, the best prediction models were developed for P (0.51), B (0.50) and Zn (0.44), associated with the short-wave infra-red (SWIR) region (1250–2500 nm). The results of this study demonstrated the potential of hyperspectral techniques to be used as a base for performing initial evaluations in the field of quality traits in alfalfa crops.	en
dc.description.sponsorship	Angie L. Gámez is the recipient of a PhD grant (reference 0011-1408- 2020-000005) funded by the Government of Navarre and Nafosa S.L. This manuscript has been conducted within the context of the CropEqualT-CEC project funded by the European Union’s Horizon 2020, Belgium Marie Curie Rise research and innovation programme.	en
dc.format.mimetype	application/pdf	en
dc.format.mimetype	application/msword	en
dc.identifier.citation	Gámez, A. L., Vatter, T., Santesteban, L. G., Araus, J. L., Aranjuelo, I. (2024) Onfield estimation of quality parameters in alfalfa through hyperspectral spectrometer data. Computers and Electronics in Agriculture, 216, 1-11. https://doi.org/10.1016/j.compag.2023.108463.	en
dc.identifier.doi	10.1016/j.compag.2023.108463
dc.identifier.issn	0168-1699
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/48008
dc.language.iso	eng	en
dc.publisher	Elsevier	en
dc.relation.ispartof	Computers and Electronics in Agriculture 216, 2024, 108463	en
dc.relation.projectID	info:eu-repo/grantAgreement/Gobierno de Navarra//0011-1408- 2020-000005	en
dc.relation.publisherversion	https://doi.org/10.1016/j.compag.2023.108463
dc.rights	© 2023 The Authors. This is an open access article under the CC BY-NC-ND license.	en
dc.rights.accessRights	Acceso abierto / Sarbide irekia	es
dc.rights.accessRights	info:eu-repo/semantics/openAccess	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject	Alfalfa	en
dc.subject	Canopy	en
dc.subject	Hyperspectral technique	en
dc.subject	Quality parameters	en
dc.subject	Trait prediction	en
dc.title	Onfield estimation of quality parameters in alfalfa through hyperspectral spectrometer data	en
dc.type	Artículo / Artikulua	es
dc.type	info:eu-repo/semantics/article	en
dc.type.version	Versión publicada / Argitaratu den bertsioa	es
dc.type.version	info:eu-repo/semantics/publishedVersion	en
dspace.entity.type	Publication
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