Machine learning-optimized spectral indices for high-throughput phenotyping of chlorophyll and yield of wheat breeding lines under salinity stress conditions
4 affiliations
- 1Department of Plant Production, College of Food and Agriculture Sciences, King Saud University
- 2Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City
- 3Agricultural Engineering Department, Faculty of Agriculture, Mansoura University
- 4Precision Agriculture Laboratory, School of Life Sciences, Technical University of Munich
Abstract
High-throughput phenotyping is key in modern breeding for rapidly and cost-effectively evaluating salt-adaptive traits. However, few studies have combined spectral reflectance indices (SRIs) with deep learning to assess field-grown wheat under salt stress. In this study, we developed optimized 2D and 3D SRIs integrated with artificial neural network (ANN) to assess chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), and grain yield (GY) in 32 recombinant inbred lines (RILs) and four cultivars under 150 mM NaCl field conditions. ANOVA revealed that genotype contributed the largest proportion of the treatment sum of squares across all traits (60–80%), followed by the genotype × year interaction (8–15%), whereas year contributed the smallest proportion (1–5%). Heatmap clustering of these traits clearly distinguished salt-tolerant from salt-sensitive genotypes. The study findings highlight using four key traits as screening criteria for salt tolerance in wheat. Our optimized 2D/3D spectral indices showed moderate to strong predictive power (R2 = 0.25–0.75), outperforming earlier indices. Multi-season data improved accuracy by 15–25%, with best predictions for Chl a and TChl (R2 = 0.34–0.75) versus Chl b and GY (R2 = 0.25–0.64). Top models included ANN-3D-SRIs-8 for Chl a (R2 = 0.735/0.644), ANN-3D-SRIs-3 for Chl b (R2 = 0.611/0.549), ANN-2D-3D-SRIs-3 for TChl (R2 = 0.713/0.619), and ANN-2D-SRIs-2 for GY (R2 = 0.648/0.553). This framework combines optimized indices and machine learning for scalable, high-throughput phenotyping to advance precision breeding of salt-tolerant wheat.
Related
- User-friendly software for high-throughput processing of chlorophyll fluorescence images to assess plant stress responses: fvfmPy and fvfmR packages
- Closing the phenotyping gap: non-invasive electromagnetic induction enables high-throughput screening of crop root function in the field
- Model-assisted high-throughput phenotyping of photosynthetic acclimation
- Non-destructive estimation of individual buckwheat plant biomass using high-throughput phenotyping-derived canopy traits
- Physiology-informed high-throughput phenotyping of grain moisture dynamics provides enhanced insights into rice grain weight formation
BibTeX
@article{alotaibi2026machine,
title = {{Machine learning-optimized spectral indices for high-throughput phenotyping of chlorophyll and yield of wheat breeding lines under salinity stress conditions}},
author = {Majed Alotaibi and Muhammad Usman Tahir and Muhammad Mubushar and Salah Elsayed and Osama Elsherbiny and Yuncai Hu and Nasser Al-Suhaibani and Salah El-Hendawy},
journal = {Scientific Reports},
year = {2026},
month = aug,
doi = {10.1038/s41598-026-64692-5},
url = {https://doi.org/10.1038/s41598-026-64692-5},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine learning-optimized spectral indices for high-throughput phenotyping of chlorophyll and yield of wheat breeding lines under salinity stress conditions
AU - Majed Alotaibi
AU - Muhammad Usman Tahir
AU - Muhammad Mubushar
AU - Salah Elsayed
AU - Osama Elsherbiny
AU - Yuncai Hu
AU - Nasser Al-Suhaibani
AU - Salah El-Hendawy
PY - 2026
DA - 2026/08/01
JO - Scientific Reports
DO - 10.1038/s41598-026-64692-5
UR - https://doi.org/10.1038/s41598-026-64692-5
AB - High-throughput phenotyping is key in modern breeding for rapidly and cost-effectively evaluating salt-adaptive traits. However, few studies have combined spectral reflectance indices (SRIs) with deep learning to assess field-grown wheat under salt stress. In this study, we developed optimized 2D and 3D SRIs integrated with artificial neural network (ANN) to assess chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), and grain yield (GY) in 32 recombinant inbred lines (RILs) and four cultivars under 150 mM NaCl field conditions. ANOVA revealed that genotype contributed the largest proportion of the treatment sum of squares across all traits (60–80%), followed by the genotype × year interaction (8–15%), whereas year contributed the smallest proportion (1–5%). Heatmap clustering of these traits clearly distinguished salt-tolerant from salt-sensitive genotypes. The study findings highlight using four key traits as screening criteria for salt tolerance in wheat. Our optimized 2D/3D spectral indices showed moderate to strong predictive power (R2 = 0.25–0.75), outperforming earlier indices. Multi-season data improved accuracy by 15–25%, with best predictions for Chl a and TChl (R2 = 0.34–0.75) versus Chl b and GY (R2 = 0.25–0.64). Top models included ANN-3D-SRIs-8 for Chl a (R2 = 0.735/0.644), ANN-3D-SRIs-3 for Chl b (R2 = 0.611/0.549), ANN-2D-3D-SRIs-3 for TChl (R2 = 0.713/0.619), and ANN-2D-SRIs-2 for GY (R2 = 0.648/0.553). This framework combines optimized indices and machine learning for scalable, high-throughput phenotyping to advance precision breeding of salt-tolerant wheat.
SN - 2045-2322
ER -