  Digitisation of fruit trees using LiDAR enables analysis which can be used to
better growing practices to improve yield. Sophisticated analysis requires
geometric and semantic understanding of the data, including the ability to
discern individual trees as well as identifying leafy and structural matter.
Extraction of this information should be rapid, as should data capture, so that
entire orchards can be processed, but existing methods for classification and
segmentation rely on high-quality data or additional data sources like cameras.
We present a method for analysis of LiDAR data specifically for individual tree
location, segmentation and matter classification, which can operate on
low-quality data captured by handheld or mobile LiDAR. Our methods for tree
location and segmentation improved on existing methods with an F1 score of
0.774 and a v-measure of 0.915 respectively, while trunk matter classification
performed poorly in absolute terms with an average F1 score of 0.490 on real
data, though consistently outperformed existing methods and displayed a
significantly shorter runtime.
