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

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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.

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Acknowledgements

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.

Funding

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.

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Authors and Affiliations

  1. Department of Mathematics, University of Extremadura, 10003, Cáceres, Spain

    Judit Rubio-Delgado & Carlos J. Pérez

  2. Department of Computer and Communications Technologies, University of Extremadura, 10003, Cáceres, Spain

    Miguel A. Vega-Rodríguez

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  1. Judit Rubio-Delgado
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  2. Carlos J. Pérez
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Correspondence to Judit Rubio-Delgado.

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Rubio-Delgado, J., Pérez, C.J. & Vega-Rodríguez, M.A. Predicting leaf nitrogen content in olive trees using hyperspectral data for precision agriculture. Precision Agric 22, 1–21 (2021). https://doi.org/10.1007/s11119-020-09727-1

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