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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Precision Agricultur...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Precision Agriculture
Article . 2020 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Recolector de Ciencia Abierta, RECOLECTA
Article . 2024 . Peer-reviewed
License: CC BY SA
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Predicting leaf nitrogen content in olive trees using hyperspectral data for precision agriculture

Authors: Judit Rubio-Delgado; Carlos J. Pérez; Miguel A. Vega-Rodríguez;

Predicting leaf nitrogen content in olive trees using hyperspectral data for precision agriculture

Abstract

Olive orchard is one of the main crops in the Mediterranean basin and, particularly, in Spain, with 56% of European production. In semi-arid regions, nitrogen (N) is the main limiting factor of olive trees after water and its quantification is essential to carry out accurate fertilization planning. In the present study, N status of an olive orchard located in Carmonita (southwest Spain) was analysed using hyperspectral data. Reflectance data were recorded with a high precision spectro-radiometer through the full spectrum (350–2500 nm). Different vegetation indices (VI), combining two or three wavelengths, and partial least squares regression (PLSR) models were developed, and the prediction capabilities were compared. Different pre-processing (smoothing, SM; standard normal variate, SNV; first and second derivative) were applied to analyse the influence of the noise generated by the spectro-radiometer measurements when computing the determination coefficient between leaf N content (LNC) and spectra data. Results showed that second derivative combined with SNV pre-processing produced the best determination coefficients. The wavelengths most sensitive to N variation used to perform VI were selected from the visible and the short-wave infrared spectrum regions, which relate to chlorophyll a+b and N absorption features. DCNI and TCARI showed the best fittings for the LNC prediction (R2=0.72, R2cv=0.71; and R2=0.64, R2cv=0.63, respectively). PLSR models yielded higher accuracy than the models based on VI (R2=0.98, R2cv=0.56), although the large difference between calibration and cross-validation showed more uncertainty in the PLSR models.

The authors wish to thank the GeoAmbiental Research Group of the University of Extremadura (Spain) for providing the spectro-radiometer used in the spectral data collection. In addition, the authors would like to thank the three anonymous referees for helping to improve both the readability and the content of this paper. This research has been financially supported by Junta de Extremadura, Spain (projects GR18090 and GR18108), European Union (European Regional Development Funds), and NotAnts S.L.U. through the project AA-16-0091-1

Versión aceptada del artículo publicado en la revista "Precision Agriculture" (Springer), volumen 22, número 1, páginas 1 - 21

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Keywords

Leaf nutritional status, Partial Least Squares Regression, 12 Matemáticas, Linear regression, Olive orchards, Nitrogen indices, SWIR spectral region

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
40
Top 10%
Top 10%
Top 10%
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