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

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.

Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis

“…Analysis of hyperspectral reflectance data using an appropriate statistical procedure is still a critical step in unraveling the relationship between these data and specific crop variables 38,39 . A PLSR analysis is one of the most efficient statistical procedures to determine the appropriateness of this relationship.…”

“…Although the parameters related to the photosynthetic efficiency have gained importance when evaluating the salt tolerance of wheat genotypes, estimation of these parameters using PLSR analysis for the full spectrum range (350–2500 nm) has been less studied. Most of the literature has focused on the performance of spectral reflectance indices (SRIs) for estimating these parameters under different abiotic stresses 38,39,44–46 . Under different levels of soil water conditions in olive orchards, Lobos et al .…”

Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”

“…The methods are broadly classified into two types, and each was applied to the two above mentioned types of phenotyping traits. For biochemical traits, machine learning and artificial intelligence methods, including principal component analysis (PCA), partial least squares regression (PLSR), random forest regression (RF), artificial neural network (ANN), etc., were usually preferred and recommended [10,[14][15][16][17][18]21,23,24,32]. Those methods proved to be very efficient for the various target phenotyping traits.…”

“…Other morphological traits such as crown height, extent, volume, and diameter were mainly derived and processed from three-dimensional point clouds by structure from motion algorithms [8,19,30,31,33]. artificial neural network (ANN), etc., were usually preferred and recommended [10,[14][15][16][17][18]21,[23][24]32]. Those methods proved to be very efficient for the various target phenotyping traits.…”

“…For ground platform, ground fixed scanning system [15,22,30,35], handheld-based field measuring [11,14,16,32], mobile ground platform (MGP) [14,20], and lifting hoist-based elevated platform [12,36] were reported for different crops types ( Figure 2). These platforms were easy-to-use with low cost, but data acquisition was semi-automatic.…”