ArticlePlant phenomics (Washington, D.C.)2026
Unlocking almond breeding for nutritional composition with hyperspectral imaging.
Article in Plant phenomics (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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