Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce

Lara, M.A. and Diezma Iglesias, Belen and Lleó García, Lourdes and Roger, Jean-Michel and Garrido, Y. and Gil, M. I. and Ruiz-Altisent, Margarita (2016). Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce. "Applied Sciences-Basel", v. 6 (n. 12); pp. 2-18. ISSN 2076-3417. https://doi.org/10.3390/app6120412.

Description

Title: Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce
Author/s:
  • Lara, M.A.
  • Diezma Iglesias, Belen
  • Lleó García, Lourdes
  • Roger, Jean-Michel
  • Garrido, Y.
  • Gil, M. I.
  • Ruiz-Altisent, Margarita
Item Type: Article
Título de Revista/Publicación: Applied Sciences-Basel
Date: 2016
ISSN: 2076-3417
Volume: 6
Subjects:
Faculty: E.T.S. de Ingeniería Agronómica, Alimentaria y de Biosistemas (UPM)
Department: Ingeniería Agroforestal
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Salinity is one of the most important stress factors in crop production, particularly in arid regions. This research focuses on the effect of salinity on the growth of lettuce plants; three solutions with different levels of salinity were considered and compared (S1 = 50, S2 = 100 and S3 = 150 mM NaCl) with a control solution (Ct = 0 mM NaCl). The osmotic potential and water content of the leaves were measured, and hyperspectral images of the surfaces of 40 leaves (10 leaves per treatment) were taken after two weeks of growth. The mean spectra of the leaves (n = 32,000) were pre-processed by means of a Savitzky?Golay algorithm and standard normal variate normalization. Principal component analysis was then performed on a calibration set of 28 mean spectra, yielding an initial model for salinity effect detection. A second model was subsequently proposed based on an index computing an approximation to the second derivative at the red edge region. Both models were applied to all the hyperspectral images to obtain the corresponding artificial images, distinguishing between the 28 that were used to extract the calibration mean spectra and the rest that constituted an external validation. Those virtual images were studied using analysis of variance in order to compare their ability for detecting salinity effects on the leaves. Both models showed significant differences between each salinity level, and the hyperspectral images allowed observations of the distribution of the salinity effects on the leaf surfaces, which were more intense in the areas distant from the veins. However, the index-based model is simpler and easier to apply because it is based solely on the reflectance at three different wavelengths, thus allowing for the implementation of less expensive multispectral devices.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainAGL2008-05666-C02-01MultihortUnspecifiedUnspecified
Government of SpainAGL2013-48529-RQualityLeafUnspecifiedBiomarcadores de calidad y seguridad de hortalizas de hoja

More information

Item ID: 44514
DC Identifier: http://oa.upm.es/44514/
OAI Identifier: oai:oa.upm.es:44514
DOI: 10.3390/app6120412
Official URL: https://www.mdpi.com/2076-3417/6/12/412
Deposited by: Memoria Investigacion
Deposited on: 02 Feb 2017 16:41
Last Modified: 05 Jun 2019 09:10
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