LiDAR Is Effective in Characterizing Vine Growth and Detecting Associated Genetic Loci

Abstract

The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard. High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period. We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross, between IJ119, a local genitor, and Divona, both in summer and in winter, using several methods: fresh pruning wood weight, exposed leaf area calculated from digital images, leaf chlorophyll concentration, and LiDAR-derived apparent volumes. Using high-density genetic information obtained by the genotyping by sequencing technology (GBS), we detected 6 regions of the grapevine genome [quantitative trait loci (QTL)] associated with the variations of the traits in the progeny. The detection of statistically significant QTLs, as well as correlations (R²) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine. Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high, above 0.66, and stable between growing seasons. These variables provided genetic models explaining up to 47% of the phenotypic variance, which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements. Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.

Fig. 1.

Overview of the experimental and technical design of the study.

Fig. 2.

Images and point clouds for an elementary plot of Chardonnay. (A) Assembly of 3 photos taken on 2020 August 4. (B) Point cloud reconstructed from LiDAR sensors on 2020 August 5 (véraison). (C) Point cloud before pruning (2021 February 9). (D) Point cloud after pruning (2021 March 17).

Fig. 3.

Overview of the system carrying the LiDAR sensors. The vertical distance between the LiDAR sensors was 0.70 m (RGB images for this study were obtained manually).

Fig. 4.

Example of a voxelized point cloud. Elementary plot 32305, genotype 1424S, bottom LiDAR sensor, north side of the row, before pruning. 2020/21 winter.

Fig. 5.

Relationships between apparent volumes calculated with LiDAR data and (A) exposed leaf area and (B) pruning fresh weight for the 2020/2021 growing season.

Fig. 6.

Diagram showing the coefficients of determination R² between relevant variables. All the relationships were statistically significant at least at P < 0.001. PW, pruning weight; ELA, exposed leaf area calculated from RGB images. Green, variables for the canopy in summer; yellow, variables for the dormant tissues in winter.

Fig. 7.

Segregations observed for 4 traits for the 2020/2021 growing season. Blue arrow, IJ119; green arrow, Divona. All these segregations did not diverge significantly (P > 0.05) from a normal distribution according to a Shapiro–Wilk test (Table S4).

Fig. 8.

Positions of the main QTL detected on the consensus map. Only QTLs detected for at least 2 years are presented. Vertical lines, Bayesian credible interval with 0.95 probability of coverage; horizontal line, position of the LOD peak. ELA, exposed leaf area; PW, pruning weight; SPAD, chlorophyll content measured with a Konica-Minolta SPAD 502; ACV, apparent canopy volume; AWV, apparent wood volume.