Early estimation of glutelin to gliadin ratio in wheat grain using high-dimensional and hyperspectral reflectance

• The Glu/Gli in wheat grains using hyperspectral reflectance was estimated during early reproductive stages. • Integration of multiple VIs enhances accuracy in Glu/Gli estimation. • Mid grain-filling stage produces most precise Glu/Gli estimates. • RFR accurately estimated Glu/Gli combined with VIs, with an R 2 of 0.691, rRMSE of 0.096, RPD of 1.872 and RER of 6.028. Precise and timely estimation of the glutelin-to-gliadin ratio (Glu/Gli) in wheat grain is pivotal for crop monitoring, as it is a crucial quality indicator ensuring the production of high-quality wheat flour. Despite the recognized potential of hyperspectral technology in crop phenotype estimation, its application to estimate Glu/Gli in wheat grains faces challenges due to complex spectral-chemical relationships and the influence of growing seasons. This study addresses this gap by cultivating 11 wheat varieties and collecting high-dimensional hyperspectral data from field experiments during various growth stages of wheat (2018–2019 and 2019–2020). Utilizing vegetation indices (VIs) in conjunction with linear mixed-effects model (LMM) and random forest regression model (RFR), it constructs a robust Glu/Gli estimation model (with a Glu/Gli range of 1.063 to 2.218). Results reveal that a singular VI application suffers from data limitations, while the integration of multiple VIs significantly enhances estimation accuracy. The mid-grain filling period emerges as a critical stage for accurate Glu/Gli estimation, with TCARI (transformed chlorophyll absorption reflectance index) demonstrating notable significance and high correlation. In model performance, RFR (R 2 = 0.691, rRMSE = 0.096, RPD = 1.872, RER = 6.028) outperforms LMM (R 2 = 0.477, rRMSE = 0.131, RPD = 1.383, RER = 4.453), exhibiting superior accuracy in estimating grain Glu/Gli for diverse wheat varieties. This study introduces a rapid and accurate approach for early wheat grain Glu/Gli estimation, offering valuable insights for wheat value chain and precision farming.