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Appl. Sci. 2016, 6(12), 412; doi:10.3390/app6120412

Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce

1
LPF-TAGRALIA, Departamento de Ingeniería Agroforestal, E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, CEI-Moncloa. Avda. Complutense s/n, Madrid 28040, Spain
2
IRSTEA, 361 Rue Jean-François Breton BP 5095, Montpellier 34196, France
3
CEBAS-CSIC, Research Group on Quality, Safety and Bioactivity of Plant Foods, Department of Food Science and Technology, P.O. Box 164, Espinardo 30100, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Kuanglin Kevin Chao
Received: 23 October 2016 / Revised: 20 November 2016 / Accepted: 30 November 2016 / Published: 7 December 2016
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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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. View Full-Text
Keywords: hyperspectral imaging; lettuce; salinity; non-destructive assessment; salinity index hyperspectral imaging; lettuce; salinity; non-destructive assessment; salinity index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lara, M.Á.; Diezma, B.; Lleó, L.; Roger, J.M.; Garrido, Y.; Gil, M.I.; Ruiz-Altisent, M. Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce. Appl. Sci. 2016, 6, 412.

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