Adaptive Per-Tree Canopy Volume Estimation Using Mobile LiDAR in Structured and Unstructured Orchards
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916786937528320 |
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| author | Abedi, Ali Cladera, Fernando Farajijalal, Mohsen Ehsani, Reza |
| author_facet | Abedi, Ali Cladera, Fernando Farajijalal, Mohsen Ehsani, Reza |
| contents | We present a real-time system for per-tree canopy volume estimation using mobile LiDAR data collected during routine robotic navigation. Unlike prior approaches that rely on static scans or assume uniform orchard structures, our method adapts to varying field geometries via an integrated pipeline of LiDAR-inertial odometry, adaptive segmentation, and geometric reconstruction. We evaluate the system across two commercial orchards, one pistachio orchard with regular spacing and one almond orchard with dense, overlapping crowns. A hybrid clustering strategy combining DBSCAN and spectral clustering enables robust per-tree segmentation, achieving 93% success in pistachio and 80% in almond, with strong agreement to drone derived canopy volume estimates. This work advances scalable, non-intrusive tree monitoring for structurally diverse orchard environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08061 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Per-Tree Canopy Volume Estimation Using Mobile LiDAR in Structured and Unstructured Orchards Abedi, Ali Cladera, Fernando Farajijalal, Mohsen Ehsani, Reza Robotics Computer Vision and Pattern Recognition Systems and Control We present a real-time system for per-tree canopy volume estimation using mobile LiDAR data collected during routine robotic navigation. Unlike prior approaches that rely on static scans or assume uniform orchard structures, our method adapts to varying field geometries via an integrated pipeline of LiDAR-inertial odometry, adaptive segmentation, and geometric reconstruction. We evaluate the system across two commercial orchards, one pistachio orchard with regular spacing and one almond orchard with dense, overlapping crowns. A hybrid clustering strategy combining DBSCAN and spectral clustering enables robust per-tree segmentation, achieving 93% success in pistachio and 80% in almond, with strong agreement to drone derived canopy volume estimates. This work advances scalable, non-intrusive tree monitoring for structurally diverse orchard environments. |
| title | Adaptive Per-Tree Canopy Volume Estimation Using Mobile LiDAR in Structured and Unstructured Orchards |
| topic | Robotics Computer Vision and Pattern Recognition Systems and Control |
| url | https://arxiv.org/abs/2506.08061 |