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

Lara, M.A.; Diezma Iglesias, Belen; Lleó García, Lourdes; Roger, Jean-Michel; Garrido, Y.; Gil, M. I. y 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/app6120401.

Descripción

Título: Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce
Autor/es:
  • Lara, M.A.
  • Diezma Iglesias, Belen
  • Lleó García, Lourdes
  • Roger, Jean-Michel
  • Garrido, Y.
  • Gil, M. I.
  • Ruiz-Altisent, Margarita
Tipo de Documento: Artículo
Título de Revista/Publicación: Applied Sciences-Basel
Fecha: 2016
Volumen: 6
Materias:
Escuela: E.T.S. de Ingeniería Agronómica, Alimentaria y de Biosistemas (UPM)
Departamento: Ingeniería Agroforestal
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 44514
Identificador DC: http://oa.upm.es/44514/
Identificador OAI: oai:oa.upm.es:44514
Identificador DOI: 10.3390/app6120401
URL Oficial: http://www.mdpi.com/2076-3417/6/12/401
Depositado por: Memoria Investigacion
Depositado el: 02 Feb 2017 16:41
Ultima Modificación: 02 Feb 2017 16:41
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