Résumé
Despite previous reports on the genotypic variation of architectural and functional traits in fruit trees, phenotyping large populations in the field remains challenging. In this study, we used high-throughput phenotyping methods on an apple tree core-collection (1000 individuals) grown under contrasted watering regimes. First, architectural phenotyping was achieved using T-LiDAR scans for estimating convex and alpha hull volumes and the silhouette to total leaf area ratio (STAR). Second, a semi-empirical index (IPL) was computed from chlorophyll fluorescence measurements, as a proxy for leaf photosynthesis. Last, thermal infrared and multispectral airborne imaging was used for computing canopy temperature variations, water deficit, and vegetation indices. All traits estimated by these methods were compared to low-throughput in planta measurements. Vegetation indices and alpha hull volumes were significantly correlated with tree leaf area and trunk cross sectional area, while IPL values showed strong correlations with photosynthesis measurements collected on an independent leaf dataset. By contrast, correlations between stomatal conductance and canopy temperature estimated from airborne images were lower, emphasizing discrepancies across measurement scales. High heritability values were obtained for almost all the traits except leaf photosynthesis, likely due to large intra-tree variation. Genotypic means were used in a clustering procedure that defined six classes of architectural and functional combinations. Differences between groups showed several combinations between architectural and functional traits, suggesting independent genetic controls. This study demonstrates the feasibility and relevance of combining multi-scale high-throughput methods and paves the way to explore the genetic bases of architectural and functional variations in woody crops in field conditions. A high-throughput method for determining plant characteristics offers a viable way to track competitive traits in the field. Increasingly difficult agricultural conditions are spurring plant breeding programs to prioritize plants with strong adaptations for healthy metabolism, but no current technologies can rapidly assess plant traits in the field. Now, Benoît Pallas, of the French National Institute for Agricultural Research, and his team have adapted tools such as infrared and laser-light scanning, and drone imagery, to take define and correlate metrics of 1000 apple trees, such as measurements of volume, leaf area, photosynthetic activity, and water usage. With these results and prior knowledge of the trees’ genotype, the team was able to group the trees into six classes with distinct physical and functional trait combinations. These techniques may open doors for discovering the genetic bases of plant traits.