Predictions of wheat phenotypic variability by integrating high-throughput phenotyping observations into a crop growth model.

Abstract

Accurate prediction of phenotypes across genotypes and environments is crucial for accelerating crop improvement. Process-based crop growth models (CGMs) can capture complex genotype-by-environment interactions, but their use is limited by labor-intensive genotypic parameter measurements. Here, we developed a faster data assimilation pipeline integrating high-throughput phenotyping (HTP) observations with the SiriusQuality wheat model to efficiently estimate key genotypic parameters and predict genotype performance. Using time-series RGB imagery from a ground-based Phenomobile, we assimilated intercepted photosynthetically active radiation (fIPAR), heading date, and final grain yield to jointly assimilated to calibrate twelve genotypic parameters governing phenology, canopy development, light interception, biomass accumulation, and grain filling. Two data assimilation strategies—a Bayesian DREAM (zs) algorithm and a lookup table (LUT) inversion—were compared through both in silico experiment and eight years of multi-environment field trials of nine durum wheat cultivars. The LUT method demonstrated superior computational efficiency, with prediction accuracy comparable to Bayesian inference on real field data. Multi-year field trials showed that two environments (year / site) were sufficient to reliably characterize genotypic parameters and predict performance across environments. By combining time-series HTP data with ecophysiological modeling, our data assimilation pipeline offers breeders a powerful tool for genotype characterization. It streamlines the process of capturing environmental variance and phenotypic stability, reducing time and effort in crop improvement.

Plant phenotyping relevance

HTP画像を作物成長モデルへ統合するデータ同化パイプラインを開発し、複数アルゴリズムと実圃場データで性能比較・検証しており、表現型取得・推定手法が研究の中心である。

Code and data availability

The paper's phenotype datasets (fIPAR, GAI, heading date, yield from eight years of Phenomobile field trials) and analysis code are not publicly deposited; the Data availability statement says they are available from the corresponding authors on reasonable request. The SiriusQuality GitHub link is the pre-existing crop

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