Leaf Identification in High-Density LiDAR-RGB Data

Leaf detection through automated segmentation of 3D data is becoming a crucial technique in many applications of digital agriculture. Some 3D segmentation techniques that can be mentioned are based on normal differences and median normalised vector growth. However, applying these approaches to high canopy density data remains challenging. In this study, we propose a processing flow for leaf detection in high canopy density LiDAR-RGB point clouds. First, a noise removal technique inspired by Moving Least Squares (MLS) was applied to the LiDAR point cloud, and a RGB colour was assigned to each point by combining computer vision and photogrammetric methods. Moreover, once the data were suitable for leaf detection, the branches were filtered using the Statistical Outlier Removal (SOR) filter based on an analysis of the statistical behaviour of the neighbourhood. Afterwards, an unsupervised DBSCAN (Density-Based Clustering Non-Parametric Algorithm) method was used to segment similar points. Finally, the points within each cluster were identified as leaf or non-leaf using the RGB values implemented by our method; ground points were filtered out using a maximum height threshold. As a result, the leaf, non-leaf, and ground point identifiers were correct in 98.9% of cases, with the branch filtering technique SOR proving effectiveness in removing branches with low information loss and without additional complex point densification steps in reconstruction. This SOR-based solution overcomes major challenges in semantic segmentation (leaves and branches) in high-density data and potentially contributes to precision agriculture.