Approach for graph-based individual branch modelling of meadow orchard trees with 3D point clouds

Abstract

The cultivation of meadow orchards provides an ecological benefit for biodiversity, which is significantly higher than in intensively cultivated orchards. However, the maintenance of meadow orchards is not economically profitable. The use of automation for pruning would reduce labour costs and avoid accidents. The goal of this research was, using photogrammetric point clouds, to automatically calculate tree models, without additional human input, as basis to estimate pruning points for meadow orchard trees. Pruning estimates require a knowledge of the major tree structure, containing the branch position, the growth direction and their topological connection. Therefore, nine apple trees were captured photogrammetrically as 3D point clouds using an RGB camera. To extract the tree models, the point clouds got filtered with a random forest algorithm, the trunk was extracted and the resulting point clouds were divided into numerous K-means clusters. The cluster centres were used to create skeleton models using methods of graph theory. For evaluation, the nodes and edges of the calculated and the manually created reference tree models were compared. The calculated models achieved a producer’s accuracy of 73.67% and a user's accuracy of 74.30% of the compared edges. These models now contain the geometric and topological structure of the trees and an assignment of their point clouds, from which further information, such as branch thickness, can be derived on a branch-specific basis. This is necessary information for the calculation of pruning areas and for the actual pruning planning, needed for the automation of tree pruning.