Crop height determination with UAS point clouds

36 citations as recorded by crossref.

1. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery L. Malambo et al. https://doi.org/10.1016/j.jag.2017.08.014
2. UAV‐based imaging platform for monitoring maize growth throughout development S. Tirado et al. https://doi.org/10.1002/pld3.230
3. Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research Y. Shi et al. https://doi.org/10.1371/journal.pone.0159781
4. Nitrogen and chlorophyll status in romaine lettuce using spectral indices from RGB digital images R. Mendoza-Tafolla et al. https://doi.org/10.13080/z-a.2021.108.011
5. Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography J. Wu et al. https://doi.org/10.1186/s13007-022-00966-z
6. Cotton Growth Modelling Using UAS-Derived DSM and RGB Imagery V. Psiroukis et al. https://doi.org/10.3390/rs15051214
7. Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding P. Herzig et al. https://doi.org/10.3390/rs13142670
8. Crop height estimation of sorghum from high resolution multispectral images using the structure from motion (SfM) algorithm E. Tunca et al. https://doi.org/10.1007/s13762-023-05265-1
9. Calibrating ultrasonic sensor measurements of crop canopy heights: a case study of maize and wheat Y. Zheng et al. https://doi.org/10.3389/fpls.2024.1354359
10. A comparative analysis of 3D point clouds and crop surface models for rice plant height estimation using UAV-SfM K. Zhang et al. https://doi.org/10.3389/fsufs.2026.1913485
11. A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes G. Galli et al. https://doi.org/10.1007/s00122-020-03726-6
12. Mobile low-cost 3D camera maize crop height measurements under field conditions M. Hämmerle & B. Höfle https://doi.org/10.1007/s11119-017-9544-3
13. Estimating plant height, nitrogen uptake and above-ground biomass using UAV multispectral imaging coupled with machine learning in industrial hemp (Cannabis sativa L.) A. Singh et al. https://doi.org/10.1016/j.indcrop.2025.122130
14. Improved Estimation of Cotton Aboveground Biomass Using a New Developed Multispectral Vegetation Index and Particle Swarm Optimization G. Wu et al. https://doi.org/10.3390/agriculture15242608
15. High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates S. Madec et al. https://doi.org/10.3389/fpls.2017.02002
16. Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery W. Wang et al. https://doi.org/10.3390/agriculture16182034
17. Winter Wheat Canopy Height Extraction from UAV-Based Point Cloud Data with a Moving Cuboid Filter Y. Song & J. Wang https://doi.org/10.3390/rs11101239
18. Growth, morphological and yield responses of irrigated wheat (Triticum aestivum L.) genotypes to water stress U. Liwani et al. https://doi.org/10.1080/09064710.2019.1577481
19. Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras H. Cen et al. https://doi.org/10.1186/s13007-019-0418-8
20. UAV photogrammetry for mapping vegetation in the low-Arctic R. Fraser et al. https://doi.org/10.1139/as-2016-0008
21. Geometric vs spectral content of Remotely Piloted Aircraft Systems images in the Precision agriculture context F. Sarvia et al. https://doi.org/10.1016/j.ejrs.2024.06.003
22. Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits D. Sweet et al. https://doi.org/10.1111/tpj.17092
23. Density estimation method of mature wheat based on point cloud segmentation and clustering R. Zou et al. https://doi.org/10.1016/j.compag.2023.107626
24. Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass R. Kümmerer et al. https://doi.org/10.3390/rs15061520
25. An Efficient Approach Based on UAV Orthographic Imagery to Map Paddy With Support of Field-Level Canopy Height From Point Cloud Data H. Liu et al. https://doi.org/10.1109/JSTARS.2018.2829218
26. Influence of Spiral Nematodes (Scutellonema spp.) on Maize Performance and Growth under Natural Field Infestation in Mwea, Kenya S. Maina & R. Nyoike Ng’endo https://doi.org/10.1155/2020/9587569
27. Photogrammetry for assessment of pasture biomass K. Wigley et al. https://doi.org/10.33584/jnzg.2019.81.394
28. Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment C. Stanton et al. https://doi.org/10.1117/1.JRS.11.026035
29. Field estimation of maize plant height at jointing stage using an RGB-D camera R. Qiu et al. https://doi.org/10.1016/j.cj.2022.07.010
30. Plant height measurement using UAV-based aerial RGB and LiDAR images in soybean L. Pun Magar et al. https://doi.org/10.3389/fpls.2025.1488760
31. Low-Cost Lettuce Height Measurement Based on Depth Vision and Lightweight Instance Segmentation Model Y. Zhao et al. https://doi.org/10.3390/agriculture14091596
32. Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging E. Grüner et al. https://doi.org/10.3390/agronomy9020054
33. Direct derivation of maize plant and crop height from low-cost time-of-flight camera measurements M. Hämmerle & B. Höfle https://doi.org/10.1186/s13007-016-0150-6
34. Spatio-temporal evaluation of plant height in corn via unmanned aerial systems S. Varela et al. https://doi.org/10.1117/1.JRS.11.036013
35. Random forests to detect subsoiling and subsurface drainage effects on corn plant height and water table depth A. Djiemon et al. https://doi.org/10.1016/j.still.2019.01.007
36. Field phenotyping of plant height in an upland rice field in Laos using low-cost small unmanned aerial vehicles (UAVs) K. Kawamura et al. https://doi.org/10.1080/1343943X.2020.1766362