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.

使用 LiDAR 对果树进行数字化可以进行分析，这些分析可用于更好的种植实践以提高产量。复杂的分析需要对数据的几何和语义理解，包括识别单个树木以及识别叶状和结构物质的能力。这些信息的提取应该是快速的，数据捕获也应该是快速的，以便可以处理整个果园，但现有的分类和分割方法依赖于高质量数据或其他数据源，如相机。我们提出了一种分析 LiDAR 数据的方法，专门用于单个树木的位置、分割和物质分类，该方法可以对手持或移动 LiDAR 捕获的低质量数据进行操作。我们的树定位和分割方法改进了现有方法，F1 分数为 0。