Detection of Pinus Radiata Cutpoints Using Neural Medial Axis Skeletonization

This paper introduces a method for Pinus Radiata cutpoint modelling and detection that, to the best of our knowledge, offers the first solution capable of determining high-precision pruning cutpoints for autonomous forestry applications. We utilise a sparse convolutional neural network to produce a tree skeleton from 3D point cloud data, then determine branch collar regions and cutpoints using a novel detection algorithm based on real-world forest measurements. Our proposed method is robust in complex environments, producing branch cutpoints even through occlusions from pine needles and other fine structures, and achieving cutpoint detection times of around 500 ms. We evaluate our method using a synthetic tree dataset and real P. Radiata forest data, with cutpoint estimates assessed against industry 'best practices' for P. Radiata forestry pruning. Our evaluation using synthetic and real-world data demonstrates the precision of our approach when compared to prior tree pruning methods.