Combining Hyperspectral Reflectance and Multivariate Regression Models to Estimate Plant Biomass of Advanced Spring Wheat Lines in Diverse Phenological Stages under Salinity Conditions

Abstract

An area of growing interest in wheat-breeding programs for abiotic stresses is the accurate and expeditious phenotyping of large genotype collections using nondestructive hyperspectral sensing tools. The main goal of this study was to use data from canopy spectral signatures (CSS) in the full-spectrum range (400–2500 nm) to estimate and predict the plant biomass dry weight at booting (BDW-BT) and anthesis (BDW-AN) growth stages, and biological yield (BY) of 64 spring wheat germplasms exposed to 150 mM NaCl using 13 spectral reflectance indices (SRIs, consisting of seven vegetation-related SRIs and six water-related SRIs) and partial least squares regression (PLSR). SRI and PLSR performance in estimating plant traits was evaluated during two years at BT, AN, and early milk grain (EMG) growth stages. Results showed significant genotypic differences between the three traits and SRIs, with highly significant two-way and three-way interactions between genotypes, years, and growth stages for all SRIs. Genotypic differences in CSS and the relationships between the three traits and a single wavelength over the full-spectrum range depended on the growth stage. Water-related SRIs were more strongly correlated with the three traits compared with vegetation-related SRIs at the BT stage; the opposite was found at the EMG stage. Both types of SRIs exhibited comparable associations with the three traits at the AN stage. Principal component analysis indicated that it is possible to assess plant biomass variations at an early stage (BT) through published and modified SRIs. SRIs coupled with PLSR models at the BT stage exhibited good prediction capacity of BDW-BT (57%), BDW-AN (82%), and BY (55%). Overall, results demonstrated that the integration of SRIs and multivariate models may present a feasible tool for plant breeders to increase the efficiency of the evaluation process and to improve the genetics for salt tolerance in wheat-breeding programs.

Hyperspectral reflectance and agro-physiological traits for field identification of salt-tolerant wheat genotypes using the genotype by yield*trait biplot technique

“…The appropriate design, alpha lattice, was used to reduce the experimental error generated and analyzed by GenStat, especially, in salinity-affected fields (acquired for genotype evaluation). Findings of grain yield data presented in Table S4 (combined) over the four studied environments showed significant differences for environments, genotypes, and their interaction, which are similar to other reports ( Ali et al., 2012 ; Enyew et al., 2021 ; Msundi et al., 2021 ; El-Hendawy et al., 2022 ). Additionally, similar findings were pointed out for the combined data and GGE biplot by ( Enyew et al., 2021 ; Darwish et al., 2022 ; Darwish et al., 2023 ).…”

“…Remote sensing technologies and spectral instruments create valuable spectral information in many wavelength bands throughout the electromagnetic spectrum, particularly visible, near-infrared, and shortwave, and provide spectral reflectance indices. These approaches are becoming extremely powerful tools for identifying chemical and physical plant structures and functions by non-destructive methods and rapid and precise measurements ( Reynolds et al., 2012 ; Bruning et al., 2020 ; El-Hendawy et al., 2022 ). Additionally, GGE, GT, and GYT biplots are other powerful tools in plant breeding for screening many genotypes and identify the best one, specifically under stress conditions.…”

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

“…In recent years, corn, sugarcane, coffee, canola, wheat, and tobacco crops have garnered increased interest for plant and pigment phenotyping [ 1 , 2 , 3 , 4 , 5 ]. According to the FAO (2022), approximately 70% of global crop production comprises agronomic crops.…”

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

“…Previous research by Gitelson and Solovchenko (2018) [25] on P. quinque-folia demonstrated high values (R 2 ≥ 0.92) for chlorophylls when reflectance data collected in an integrating sphere were transformed to absorbance. However, this method selected wavelengths specific to the species studied [5,29,[48][49][50], whereas the present study used a faster and simpler method that simultaneously collected both reflectance and transmittance data using contiguous hyperspectral bands in the visible spectrum (400-700 nm). Other studies [6] resulted in the collection of 300 hyperspectral coefficients with a resolution of 1 nm and a measurement time of less than 5 s [6].…”

“…Combining hyperspectral measurements and predictions from two sensors has shown promise in improving the detection of yellow rust on winter wheat, with a high accuracy of up to 94-95% for the fluorescence method [59]. However, this approach can provide a more comprehensive and accurate assessment of plant health by capturing both structural and physiological information as well as in other agronomic plants [29,48,64]. Moreover, detecting plant diseases at an early stage can help reduce crop losses and increase yields.…”