Abstract
Point clouds and digital surface models (DSMs) derived from unmanned aircraft system (UAS) imagery are widely used for plant height estimation in plant phenotyping and precision agriculture. However, comprehensive evaluations across multiple crops, flight altitudes, and image overlaps are limited, restricting guidance for optimizing flight strategies. This study evaluated the effects of flight altitude, side and front overlap, and image processing parameters on point cloud generation and plant height estimation. UAS imagery was collected at four altitudes (30–120 m, corresponding to 0.5–2.0 cm ground sampling distance, GSD) with multiple side and front overlaps (67–94%) over a 2–ha field planted with corn, cotton, sorghum, and soybean on three dates across two growing seasons, producing 90 datasets. Orthomosaics, point clouds, and DSMs were generated using Pix4Dmapper, and plant height estimates were extracted from both DSMs and point clouds. Results showed that point clouds consistently outperformed DSMs across altitudes, overlaps, and crop types. Highest accuracy occurred at 60–90 m (1.0–1.5 cm GSD) with RMSE values of 0.06–0.10 m (R2 = 0.92–0.95) in 2019 and 0.07–0.08 m (R2 = 0.80–0.89) in 2022. Across multiple side and front overlap combinations at 60–120 m, reduced overlaps produced RMSE values comparable to full overlaps, indicating that optimized flight settings, particularly reduced side overlap with high front overlap, can shorten flight and processing time without compromising point cloud quality or height estimation accuracy. Pix4Dmapper processing parameters strongly affected 3D point cloud density (2–600 million points), processing time (1–16 h), and plant height accuracy (R2 = 0.67–0.95). These findings provide practical guidance for selecting UAS flight and processing parameters to achieve accurate, efficient 3D modeling and plant height estimation. By balancing flight altitude, image side and front overlap, and photogrammetric processing settings, users can improve operational efficiency while maintaining high-accuracy plant height measurements, supporting faster and more cost-effective phenotyping and precision agriculture applications.
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Claims 8
Claim · model-written summary
UAS point clouds consistently outperformed DSMs for plant height estimation across altitudes, overlaps and crop types.
- Taxon
- corn, cotton, sorghum, and soybean
Gossypium hirsutum
Glycine max
Sorghum
Zea mays subsp. mayse1 - Outcome
- plant height estimationt0
- Direction
- outperformede0
- Comparator
- DSMse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“point clouds consistently outperformed DSMs across altitudes, overlaps, and crop types”
t0“Plant Height Estimation”
e1“corn, cotton, sorghum, and soybean”
Claim · model-written summary
In 2019 highest plant height accuracy occurred at 60–90 m altitude (1.0–1.5 cm GSD), with RMSE of 0.06–0.10 m (R2 = 0.92–0.95).
- Taxon
- corn, cotton, sorghum, and soybeanGossypium hirsutumGlycine maxSorghumZea mays subsp. mayse1
- Outcome
- RMSE valuese0
- Value
- 0.06–0.10 me0
- Qualifier
- R2 = 0.92–0.95e0
- Setting
- 60–90 me0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Highest accuracy occurred at 60–90 m (1.0–1.5 cm GSD) with RMSE values of 0.06–0.10 m (R2 = 0.92–0.95) in 2019 and 0.07–0.08 m (R2 = 0.80–0.89) in 2022”
e1“corn, cotton, sorghum, and soybean”
Claim · model-written summary
In 2022 highest plant height accuracy at 60–90 m altitude had RMSE of 0.07–0.08 m (R2 = 0.80–0.89).
- Taxon
- corn, cotton, sorghum, and soybeanGossypium hirsutumGlycine maxSorghumZea mays subsp. mayse1
- Outcome
- RMSE valuese0
- Value
- 0.07–0.08 me0
- Qualifier
- R2 = 0.80–0.89e0
- Setting
- 60–90 me0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Highest accuracy occurred at 60–90 m (1.0–1.5 cm GSD) with RMSE values of 0.06–0.10 m (R2 = 0.92–0.95) in 2019 and 0.07–0.08 m (R2 = 0.80–0.89) in 2022”
e1“corn, cotton, sorghum, and soybean”
Claim · model-written summary
Across side and front overlap combinations at 60–120 m, reduced overlaps produced RMSE values comparable to full overlaps.
- Taxon
- corn, cotton, sorghum, and soybeanGossypium hirsutumGlycine maxSorghumZea mays subsp. mayse1
- Outcome
- RMSE valuese0
- Direction
- comparablee0
- Comparator
- full overlapse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“reduced overlaps produced RMSE values comparable to full overlaps”
e1“corn, cotton, sorghum, and soybean”
Claim · model-written summary
Pix4Dmapper processing parameters strongly affected 3D point cloud density, which ranged 2–600 million points.
- Taxon
- corn, cotton, sorghum, and soybeanGossypium hirsutumGlycine maxSorghumZea mays subsp. mayse1
- Treatment
- Pix4Dmapper processing parameterse0
- Outcome
- 3D point cloud densitye0
- Value
- 2–600 million pointse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Pix4Dmapper processing parameters strongly affected 3D point cloud density (2–600 million points), processing time (1–16 h), and plant height accuracy (R2 = 0.67–0.95)”
e1“corn, cotton, sorghum, and soybean”
Claim · model-written summary
Pix4Dmapper processing parameters strongly affected processing time, which ranged 1–16 h.
- Taxon
- corn, cotton, sorghum, and soybeanGossypium hirsutumGlycine maxSorghumZea mays subsp. mayse1
- Treatment
- Pix4Dmapper processing parameterse0
- Outcome
- processing timee0
- Value
- 1–16 he0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Pix4Dmapper processing parameters strongly affected 3D point cloud density (2–600 million points), processing time (1–16 h), and plant height accuracy (R2 = 0.67–0.95)”
e1“corn, cotton, sorghum, and soybean”
Claim · model-written summary
Pix4Dmapper processing parameters strongly affected plant height accuracy, with R2 of 0.67–0.95.
- Taxon
- corn, cotton, sorghum, and soybeanGossypium hirsutumGlycine maxSorghumZea mays subsp. mayse1
- Treatment
- Pix4Dmapper processing parameterse0
- Outcome
- plant height accuracye0
- Value
- R2 = 0.67–0.95e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Pix4Dmapper processing parameters strongly affected 3D point cloud density (2–600 million points), processing time (1–16 h), and plant height accuracy (R2 = 0.67–0.95)”
e1“corn, cotton, sorghum, and soybean”
Claim · model-written summary
The authors conclude that optimized flight settings, particularly reduced side overlap with high front overlap, can shorten flight and processing time without compromising point cloud quality or height estimation accuracy.
- Taxon
- corn, cotton, sorghum, and soybeanGossypium hirsutumGlycine maxSorghumZea mays subsp. mayse1
- Treatment
- reduced side overlap with high front overlape0
- Outcome
- flight and processing timee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“optimized flight settings, particularly reduced side overlap with high front overlap, can shorten flight and processing time without compromising point cloud quality or height estimation accuracy”
e1“corn, cotton, sorghum, and soybean”