Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce
Año:2017
Áreas de investigación
  • Agricultura
Datos
Descripción
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
Internacional
No
JCR del ISI
No
Título de la revista
Applied Sciences
ISSN
1454-5101
Factor de impacto JCR
Información de impacto
Volumen
DOI
Número de revista
6, 412
Desde la página
1
Hasta la página
18
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Miguel Angel Lara Blas (UPM)
  • Autor: Lourdes Lleo Garcia (UPM)
  • Autor: Margarita Ruiz Altisent (UPM)
  • Autor: Jean Michel Roger (IRSTEA,)
  • Autor: Belen Diezma Iglesias (UPM)
  • Autor: Yolanda Garrido (CEBAS-CSIC,)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: LPF-TAGRALIA: Técnicas Avanzadas en Agroalimentación
  • Departamento: Ingeniería Agroforestal
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