Estimation of Branch Geometry and Hierarchy in Orchard Trees for Robotic Pruning

Orchard trees are primarily cultivated for food production, but they also provide environmental and aesthetic benefits. Proper maintenance, particularly through pruning, is crucial; however, manual pruning is labor-intensive, time-consuming, and dependent on expertise, limiting its consistency on a large scale. Automated pruning reduces labor demands and enhances scalability. In another context, automated pruning encounters additional challenges owing to the complex and inconsistent geometry of trees (branch size, position, and orientation) and their hierarchy (parent-child relationships), which play a vital role in pruning decisions. To address these challenges, a pipeline for estimating tree geometry and hierarchy was proposed. Branches and trunk from a single RGB image were segmented using a custom YOLOv8 model, and key geometric and distance features were extracted through principal component analysis, which captured over 99% of the geometric variation. A genetic algorithm then infers hierarchical relationships, assisting in the recognition of branch levels and supporting biological pruning decisions. The experimental results demonstrated distinct features across the hierarchical levels, achieving an F1 score of approximately 80% and a Jaccard index exceeding 70% during hierarchical validation. These findings demonstrate the potential of the proposed method to transform visual perception into geometric and hierarchical representations of tree structure, thereby providing essential structural information to support autonomous and biologically informed pruning decisions.