Calibrating lab and field reflectance spectra for nutrient estimation in potato plants using local support vector regression models

Reem Abukmeil, Ahmad Al-Mallahi*, Felipe Campelo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This study presents a methodology based on multiple local support vector regression (SVR) to calibrate the spectra taken in the field in relative to lab-derived spectra. Laboratory based foliar spectral measurement is a common method to provide lab-derived spectra as a service where a grower sends sample leaves collected manually. The drawback of this method is being time-consuming when the samples are collected and analyzed. In contrast, in-field spectral measurements can be an alternative method capable of providing immediate readings. While both methods work based on the same priniciple, the insturmental differences as well as the conditional difference under which the instruments operate may cause differences in the spectral patterns of the same target. In this work, after developing the calibration method, we validated it by estimating NPK measurements in potato plants using in-field, lab, and field calibrated spectral measurements over two testing modes: dried and fresh. The results showed that the calibration using SVR models could minimize the percentage relative error (PRE) between lab and field spectra within the visible range by considering the influence of the neighboring wavebands up to 32 nm width which improved the alignment of the local maxima of the specral curves. Also, a substantial PRE reduction from 120 % to 20 % for some wavebands in the short-wave infrared (SWIR) region of the fresh mode was observed due to the influence of scaling within the SVR method. The calibration improved the alignment of NPK estimated values between lab and field calibrated spectra of both modes with an emphasis on its necessity to estimate nutrients in the fresh mode as the root mean square error was < 0.1 for the three elements.

Original languageEnglish
Article number100492
Number of pages11
JournalSmart Agricultural Technology
Early online date20 Jun 2024
Publication statusE-pub ahead of print - 20 Jun 2024

Bibliographical note

© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC license (

Data Access Statement

Data will be made available on request


  • Leaf reflectance
  • NPK estimation
  • Neighbor-based variable selection
  • Support vector regression
  • Vis/NIR spectra


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