Estimating tree volume is crucial for managing apple orchards, as it reflects the nutritional status and vigor of the trees. However, accurate assessment of tree volume in apple orchards is challenging due to their complex structure. This study introduces a novel method for three-dimensional volume calculation of individual apple trees. Our approach facilitates the determination of pruning severity by analyzing the limb-to-trunk volume ratio and enables the creation of detailed pre- and post-pruning maps. We utilized a lightweight multi-camera system to reconstruct 3D point clouds of the trees and developed a voxel-based algorithm for tree volume calculation. This algorithm includes steps for interior filling, edge voxel thinning, and interior refilling. We validated our algorithm on seven apple trees by comparing the calculated volumes with the ground truth, determined using the water displacement method. The results showed that our voxel-based algorithm was highly effective in accurately calculating individual tree volumes from 3D point clouds. The algorithm also demonstrated a high coefficient of determination (0.994) and a mean absolute percentage error of 2.919% in a linear regression analysis against the ground truth. Furthermore, we produced detailed tree volume and pruning severity maps for individual trees, both before and after pruning. In conclusion, this study offers an effective solution combining 3D imaging and volume calculation techniques to accurately estimate individual apple tree volumes, providing a quantitative assessment of pruning severity. 

Improved voxel-based volume estimation and pruning severity mapping of apple trees during the pruning period

3D dataset generation using virtual reality for forest biodiversity




In close‐range remote sensing data collected in a forest, occlusion often causes incomplete or sparse point cloud representations of individual trees, impeding accurate 3D reconstruction of tree architecture and estimation of tree height and volume. Recent developments in deep learning (DL) for 3D data have produced approaches for point cloud completion, which could potentially be applied to trees.

We explored the potential of a DL approach to fill gaps in dense point clouds representing the main structures of deciduous trees by applying an existing transformer‐based completion model (PoinTr). Complete point clouds are required as training data, but even dense terrestrial laser scanning (TLS) data sets contain gaps caused by occlusion, making it nearly impossible to acquire such data. We therefore investigated the ability of point cloud completion models trained on a range of synthetic data sets to handle occlusion patterns in real‐world point clouds.

Despite the limited data set, we successfully fine‐tuned a general pre‐trained completion model to fill gaps within 1 m3 segments of tree point clouds. Fine‐tuning on synthetic tree data improved the model's ability to complete tree objects compared with training on diverse artificial objects. However, the quality of the predictions was influenced by the level of sophistication of the synthetic data. Our results demonstrate that incorporating even limited real‐world TLS data during training can considerably improve completion results but may introduce additional noise in the predictions.

3D point cloud completion with DL has the potential to improve and fill gaps in point clouds of individual trees, facilitating further steps in the processing and analysis of 3D forest data.



Completing 3D point clouds of individual trees using deep learning

A Review of Software Solutions to Process Ground-based Point Clouds in Forest Applications

Automated classification of tree species using graph structure data and neural networks

Individual Tree Volume Estimation with Terrestrial Laser Scanning: Evaluating Reconstructive and Allometric Approaches

Reconstructing coniferous tree crown shape from incomplete point clouds using deep learning

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Papers

Individual Tree Volume Estimation with Terrestrial Laser Scanning: Evaluating Reconstructive and Allometric Approaches

Reconstructing coniferous tree crown shape from incomplete point clouds using deep learning

Completing 3D point clouds of individual trees using deep learning