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Remote Sens. 2019, 11(3), 329; https://doi.org/10.3390/rs11030329

Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions

1
Plant Breeding and Phenomics Center, Facultad de Ciencias Agrarias, Universidad de Talca, Casilla 747, Talca, Chile
2
Departamento de Ciencias Agrarias, Universidad Católica del Maule, Casilla 684, Curicó, Chile
3
Department of Viticulture and Oenology, Stellenbosch University, Matieland 7602, South Africa
4
Departamento de Producción Forestal y Tecnología de la Madera, Facultad de Agronomía, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo 12900, Uruguay
5
BioHybrids International Ltd., Woodley, Reading RG6 5PY, UK
*
Authors to whom correspondence should be addressed.
Received: 19 December 2018 / Revised: 28 January 2019 / Accepted: 30 January 2019 / Published: 7 February 2019
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Abstract

To overcome the environmental changes occurring now and predicted for the future, it is essential that fruit breeders develop cultivars with better physiological performance. During the last few decades, high-throughput plant phenotyping and phenomics have been developed primarily in cereal breeding programs. In this study, plant reflectance, at the level of the leaf, was used to assess several physiological traits in five Vaccinium spp. cultivars growing under four controlled conditions (no-stress, water deficit, heat stress, and combined stress). Two modeling methodologies [Multiple Linear Regression (MLR) and Partial Least Squares (PLS)] with or without (W/O) prior wavelength selection (multicollinearity, genetic algorithms, or in combination) were considered. PLS generated better estimates than MLR, although prior wavelength selection improved MLR predictions. When data from the environments were combined, PLS W/O gave the best assessment for most of the traits, while in individual environments, the results varied according to the trait and methodology considered. The highest validation predictions were obtained for chlorophyll a/b (R2Val ≤ 0.87), maximum electron transport rate (R2Val ≤ 0.60), and the irradiance at which the electron transport rate is saturated (R2Val ≤ 0.59). The results of this study, the first to model modulated chlorophyll fluorescence by reflectance, confirming the potential for implementing this tool in blueberry breeding programs, at least for the estimation of a number of important physiological traits. Additionally, the differential effects of the environment on the spectral signature of each cultivar shows this tool could be directly used to assess their tolerance to specific environments. View Full-Text
Keywords: spectroscopy; spectrometer; spectroradiometer; phenotype; gas exchange; stem water potential; V. corymbosum; V. ashei spectroscopy; spectrometer; spectroradiometer; phenotype; gas exchange; stem water potential; V. corymbosum; V. ashei
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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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Lobos, G.A.; Escobar-Opazo, A.; Estrada, F.; Romero-Bravo, S.; Garriga, M.; del Pozo, A.; Poblete-Echeverría, C.; Gonzalez-Talice, J.; González-Martinez, L.; Caligari, P. Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions. Remote Sens. 2019, 11, 329.

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