Multi-scale high-throughput phenotyping of apple architectural and functional traits in orchard reveals genotypic variability under contrasted watering regimes

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.

. List and description of indexes computed from multispectral and thermal infrared imaging. Table S2. Mixed-effect models selected for the extraction of BLUPs of genetic values. Table S3. Values of chlorophyll fluorescence related parameters for the two IRGA devices. Figure S1. Evolution of soil water potential during the experiment.      Figure S7. Correlation between imaging data collected on July 27 th and 28 th . Figure S8. Overview of the impact of water stress on imaging data on July 28 th . Figure S9. Comparison between the distribution of surface temperatures within the canopy pixels (thermal infrared imaging), and leaf temperatures measured on a single fully developed leaf (IRGA), for 8 genotypes. Figure S10. Comparison between the distribution of surface temperatures within the canopy pixels (thermal infrared imaging), and leaf temperatures measured on a single fully developed leaf (IRGA), for the whole core-collection.  Table S1. Description of indexes computed from multi-spectral and thermal infrared imaging on the core-collection.
Rxxx, the reflectance for a xxx nm wavelength.
For WDI: (Tsurf -Tair)i, the average values of (Tsurf -Tair) on all the pixels in the area of interest for the tree i, and (Tsurf -Tair)WW -(Tsurf -Tair)WD the hypothetical (Tsurf -Tair) values of pixel displaying the same NDVI than the considered trees under WW or WD conditions, respectively. In this study the four extremities of the trapezoid shape (well-watered vegetation, water-stressed vegetation, saturated bare soil, and dry bare soil) were estimated on 0.5% of the pixels with the lowest or highest NDVI. All the pixels of this selection were used whatever their origin (trees, grass, soil). This approach allows defining two lines corresponding to the minimal or maximal Tsurf -Tair values depending on NDVI variations.
References: Gamon et al. 1992 Table S2. Mixed-effect models selected for the extraction of BLUPs of genetic values. Models selected were those with the lowest Bayesian Information Criterion (BIC), among several mixed models.

Mixed-model TCSA
Trait acronyms as in Table 1. For R, three positions were defined (1, 2 and 3) since the T-LiDAR device was positioned every five trees along each row. R=1 means that the T-LiDAR was in front of the tree, R=2 was used when there was one tree between the considered tree and the T-LiDAR, and R=3 for a two tree interval between the tree and T-LiDAR.  Figure S1. Evolution of soil water potential during the experiment. Soil water potential (Ψsoil) was measured at 60 cm depth by Watermark® probes for the subset of 13 trees (7 well-watered, WW, and 6 water deficit, WD) monitored during summer 2017 in Montpellier. All trees were well-irrigated (2h everyday) until July 5 th (black line). The irrigation was then limited to 2h twice a week for the WD trees. The date when the measurements were undertaken (imagery, fluorimetry, porometry) is indicated with a black arrow. Figure S2. Zenithal images of trees in the field. Red circles above each tree represent the zone of interest (radius = 0.70m, buffer zone) on which imaging data (thermal infrared, TIR ; and multispectral, MS) were estimated. a b Figure S5. Effect of the air VPD on the measurements of IPL. IPL measurements were collected on 195 genotypes (2 well-watered (WW) and 2 water deficit (WD) trees per genotype) over two consecutive days (July 27 th and 28 th ) during a maximum period of 3 hours centered on solar midday. The air VPD was simultaneously recorded every 10 seconds by a meteorological station settled within the core-collection. a b Figure S8. Overview of the impact of water deficit on imaging data on July 28 th . (a) Relationship between surfaceto-air temperature difference (Tsurf -Tair) and NDVI from images taken on July 28 th (flight time from about 10 to 11am UTC). Grey points represent all the pixels including soil, weed and trees' foliage whereas red and blue points are the mean values of Tsurf -Tair and NDVI for each individual tree (blue: WW trees, red: WD trees). Solid lines represent the trapezoid shape used for computing WDI (see Table S1). Extremities of the trapezoid represent "well-  Figure S9. Comparison between the distribution of surface temperatures within the canopy pixels (thermal infrared imaging), and leaf temperatures measured on fully developed leaves with a leaf gas analyzer, for 8 genotypes. Boxplot of the surface-to-air temperature difference (Tsurf -Tair) of all canopy pixels, and value within this distribution of leaf-to-air temperature difference (Tleaf -Tair) measured with the gas-exchange analyzer (triangles). In each boxplot, the values of the two trees for the genotype and scenario considered are represented. The genotype name is indicated in the top-left corner of each panel. Blue and red boxplots correspond to well-watered (WW) and water deficit (WD) trees, respectively. T surf -T air (pixels) thermal imaging (°C) Figure S10. Comparison between the distribution of surface temperatures within the canopy pixels (thermal infrared imaging), and leaf temperatures measured on fully developed leaves measured with a leaf gas analyzer, for the whole core-collection. Boxplot of the surface-to-air temperature difference (Tsurf -Tair) of all canopy pixels, and value within this distribution of leaf-to-air temperature difference (Tleaf -Tair) measured with the gas-exchange analyzer (triangles). Blue and red boxplots correspond to well-watered (WW) and water deficit (WD) trees, respectively. In each boxplot, the values of the two trees for the 195 genotypes and the scenario considered are represented