Unlocking almond breeding for nutritional composition with hyperspectral imaging

High-throughput phenotyping is boosting plant breeding by generating large-scale phenotypic data for traits that were previously expensive and/or time-consuming to measure. A high-throughput phenotyping platform integrating hyperspectral imaging with a Python workflow has been developed to phenotype nutritional components in almond breeding populations, addressing the current phenotyping bottleneck of conventional methods. Kernel and powder samples from a reference set of 112 almond genotypes were scanned using a hyperspectral camera in the SWIR range (900–1700 nm) and subsequently analysed for nutritional components, including fats, protein, fiber, sucrose, fatty acids, and phytosterols. Partial Least Squares (PLS) models were developed to predict nutritional components in almond kernels, achieving cross-validation RMSE (RMSE_CV) values of 0.73, 1.28, 7.90, and 1.96, and corresponding R_CV² values of 0.82, 0.86, 0.66, and 0.57 for protein, fats, β-sitosterol, and oleic acid (C18:1), respectively. Selected PLS models were implemented to predict the nutritional components of 528 genotypes from a germplasm collection and six F₁ populations. Narrow-sense heritability for these predicted traits was estimated using an advanced linear mixed model incorporating pedigree and genomic data using the 60K Almond SNP array, revealing relevant additive effects for predicted traits (h²>0.5). The approach employed here represents a major advance in nutritional almond breeding, enabling the phenotyping of six times more individuals than previous studies and generating the largest phenotypic dataset of nutritional components in almonds and other tree nuts.