CHALLENGES AND RECOMMENDATIONS FOR 3D PLANT PHENOTYPING IN AGRICULTURE USING TERRESTRIAL LASERS SCANNERS

7 citations as recorded by crossref.

1. Reducing wind effects in multi-scan terrestrial laser scanning point clouds of trees combining LiDAR simulation and deep learning H. Weiser et al. https://doi.org/10.1093/forestry/cpag068
2. Advancing anthracnose resistance in dry beans through the transition from traditional to computational breeding efforts A. Patel et al. https://doi.org/10.3389/fpls.2026.1809294
3. Investigation of the rock mass state in the near-wall part of the quarry and its stability management P. Saik et al. https://doi.org/10.3389/feart.2024.1395418
4. Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data J. Rodriguez-Sanchez et al. https://doi.org/10.3389/fpls.2024.1436120
5. A novel approach for tropism characterisation through point cloud analysis R. Hartley et al. https://doi.org/10.33494/nzjfs562026x449x
6. MaizeField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panel E. Kimara et al. https://doi.org/10.1016/j.plaphe.2025.100108
7. A 3D printed plant model for accurate and reliable 3D plant phenotyping J. Bömer et al. https://doi.org/10.1093/gigascience/giae035