Assessing T-LiDAR technology for high throughputphenotyping apple tree topological and architectural traits

摘要

We aim at extending methodologies based on T-LiDAR scans for providing a description of individual apple tree topology and architecture. Experiments were performed on 2- and 3-year-old trees belonging to a core collection of French apple cultivars. In2016, ten trees were precisely scanned in winter to assess our ability to extract topological traits. In 2017, the whole core-collection was scanned to estimate tree leaf areas. Topological reconstructions on 2016 data were performed with Plantscan3D software using existing algorithms whereas an allometric relationship depending on tree alpha hull volumes was used for estimating tree leaf areas on 2017 data. Topological traits extracted from T-LiDAR data were compared to digitizing data and the estimated total leaf areas were compared to direct measurements. In 2016, topological reconstructions gave promising results with R2 values higher than 0.90 for the total number of axes and growth units tree-1 and for their mean length when T-Lidar were compared to digitizing dab. However, a significant number of short axes or growth units were not detected by the T-Lidar. Comparisons between T-LiDAR and digitizing data showed good adequacy if axes were classified depending on their branching order suggestingthat our method was relevant for evaluating the number of branching points in the structure. Regarding data collected on leafy trees in 2017, the total leaf area estimated from T-LiDAR was also highly correlated (R2=0.83) with manual measurements. Forthcoming works are undergoing for improving topological reconstruction algorithms adapted to the whole population. Nevertheless this first study suggests that this method could be adapted for phenotypic architectural traits on large tree population on whichgenetic analyses could be performed.