Advancements in the ability to detect plant responses to salinity are mandatory to improve crop yield, quality and management practices. The present study shows the capability of hyperspectral reflectance (400-2400 nm) to rapidly and non-destructively detect and monitor the responses of two pomegranate cultivars (Parfianka, P, and Wonderful, W) under salt treatment (i.e., 200 ml of 100 mM NaCl solution every day) for 35 days. Analyzing spectral signatures from asymptomatic leaves, the two cultivars, as well as salinity conditions, were discriminated. Furthermore, using a partial least squares regression approach, we constructed predictive models to concomitantly es-timate (R2: 0.61-0.79) from spectra various physiological leaf parameters commonly investigated in plant/salinity studies. Analyses of spectral signatures enabled the early detection of salt stress (i.e., from 14 days from the beginning of treatment, FBT), even in absence of visible symptoms, but they did not allow the identification of the different degree of salt tolerance between cultivars; this cultivar-specific tolerance to salt was instead reported by analyzing variations of leaf parameters estimated from spectra (W was less tolerant than P), which, in turn, allowed the detection of salt stress only at later times of analysis (i.e., slightly from 21 day FBT, and evidently at the end of treatment). The proposed approach could be used in precision agriculture, high-throughput plant phenotyping and smart nursery management to enhance crop quality and yield.

Hyperspectral detection and monitoring of salt stress in pomegranate cultivars

Lorenzo Cotrozzi
;
Giacomo Lorenzini;Cristina Nali;Elisa Pellegrini
2021-01-01

Abstract

Advancements in the ability to detect plant responses to salinity are mandatory to improve crop yield, quality and management practices. The present study shows the capability of hyperspectral reflectance (400-2400 nm) to rapidly and non-destructively detect and monitor the responses of two pomegranate cultivars (Parfianka, P, and Wonderful, W) under salt treatment (i.e., 200 ml of 100 mM NaCl solution every day) for 35 days. Analyzing spectral signatures from asymptomatic leaves, the two cultivars, as well as salinity conditions, were discriminated. Furthermore, using a partial least squares regression approach, we constructed predictive models to concomitantly es-timate (R2: 0.61-0.79) from spectra various physiological leaf parameters commonly investigated in plant/salinity studies. Analyses of spectral signatures enabled the early detection of salt stress (i.e., from 14 days from the beginning of treatment, FBT), even in absence of visible symptoms, but they did not allow the identification of the different degree of salt tolerance between cultivars; this cultivar-specific tolerance to salt was instead reported by analyzing variations of leaf parameters estimated from spectra (W was less tolerant than P), which, in turn, allowed the detection of salt stress only at later times of analysis (i.e., slightly from 21 day FBT, and evidently at the end of treatment). The proposed approach could be used in precision agriculture, high-throughput plant phenotyping and smart nursery management to enhance crop quality and yield.
2021
Calzone, Antonella; Cotrozzi, Lorenzo; Lorenzini, Giacomo; Nali, Cristina; Pellegrini, Elisa
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1103576
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