Calibrated plant height estimates with structure from motion from fixed-wing UAV images

2018 ◽  
Author(s):  
Xiongzhe Han ◽  
J. Alex Thomasson ◽  
Cody Bagnall ◽  
William L. Rooney ◽  
N. Ace Pugh ◽  
...  
Keyword(s):  
Plant Height ◽  
Structure From Motion ◽  
Height Estimates ◽  
Uav Images

scholarly journals A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenjuan Li ◽  
Alexis Comar ◽  
Marie Weiss ◽  
Sylvain Jay ◽  
Gallian Colombeau ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Focal Length ◽  
Critical Issue ◽  
Quality Level ◽  
Ground Sample ◽  
Height Estimates ◽  
Uav Images ◽  
Time Required ◽  
Multispectral Camera

Multispectral observations from unmanned aerial vehicles (UAVs) are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status. However, the limited autonomy of UAVs makes the completion of flights difficult when sampling large areas. Increasing the throughput of data acquisition while not degrading the ground sample distance (GSD) is, therefore, a critical issue to be solved. We propose here a new image acquisition configuration based on the combination of two focal length (f) optics: an optics with f=4.2 mm is added to the standard f=8 mm (SS: single swath) of the multispectral camera (DS: double swath, double of the standard one). Two flights were completed consecutively in 2018 over a maize field using the AIRPHEN multispectral camera at 52 m altitude. The DS flight plan was designed to get 80% overlap with the 4.2 mm optics, while the SS one was designed to get 80% overlap with the 8 mm optics. As a result, the time required to cover the same area is halved for the DS as compared to the SS. The georeferencing accuracy was improved for the DS configuration, particularly for the Z dimension due to the larger view angles available with the small focal length optics. Application to plant height estimates demonstrates that the DS configuration provides similar results as the SS one. However, for both the DS and SS configurations, degrading the quality level used to generate the 3D point cloud significantly decreases the plant height estimates.

scholarly journals Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection

2019 ◽  
Vol 7 (3) ◽  
pp. 807-827 ◽  
Author(s):  
He Zhang ◽  
Emilien Aldana-Jague ◽  
François Clapuyt ◽  
Florian Wilken ◽  
Veerle Vanacker ◽  
...  
Keyword(s):  
Change Detection ◽  
Structure From Motion ◽  
Low Cost ◽  
Post Processing ◽  
Positional Accuracy ◽  
Surface Change ◽  
Ground Control Points ◽  
Single Lens ◽  
Surface Representations ◽  
Uav Images

Abstract. Images captured by unmanned aerial vehicles (UAVs) and processed by structure-from-motion (SfM) photogrammetry are increasingly used in geomorphology to obtain high-resolution topography data. Conventional georeferencing using ground control points (GCPs) provides reliable positioning, but the geometrical accuracy critically depends on the number and spatial layout of the GCPs. This limits the time and cost effectiveness. Direct georeferencing of the UAV images with differential GNSS, such as PPK (post-processing kinematic), may overcome these limitations by providing accurate and directly georeferenced surveys. To investigate the positional accuracy, repeatability and reproducibility of digital surface models (DSMs) generated by a UAV–PPK–SfM workflow, we carried out multiple flight missions with two different camera–UAV systems: a small-form low-cost micro-UAV equipped with a high field of view (FOV) action camera and a professional UAV equipped with a digital single lens reflex (DSLR) camera. Our analysis showed that the PPK solution provides the same accuracy (MAE: ca. 0.02 m, RMSE: ca. 0.03 m) as the GCP method for both UAV systems. Our study demonstrated that a UAV–PPK–SfM workflow can provide consistent, repeatable 4-D data with an accuracy of a few centimeters. However, a few flights showed vertical bias and this could be corrected using one single GCP. We further evaluated different methods to estimate DSM uncertainty and show that this has a large impact on centimeter-level topographical change detection. The DSM reconstruction and surface change detection based on a DSLR and action camera were reproducible: the main difference lies in the level of detail of the surface representations. The PPK–SfM workflow in the context of 4-D Earth surface monitoring should be considered an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.

scholarly journals Structure from motion (SfM) processing of UAV images and combination with terrestrial laser scanning, applied for a 3D-documentation in a hazardous situation

2017 ◽  
Vol 8 (2) ◽  
pp. 1492-1504 ◽  
Author(s):  
Isabel Martínez-Espejo Zaragoza ◽  
Gabriella Caroti ◽  
Andrea Piemonte ◽  
Björn Riedel ◽  
Dieter Tengen ◽  
...  
Keyword(s):  
Structure From Motion ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
3D Documentation ◽  
Uav Images

scholarly journals Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images

2019 ◽  
Vol 11 (17) ◽  
pp. 2021 ◽  
Author(s):  
Wei Su ◽  
Mingzheng Zhang ◽  
Dahong Bian ◽  
Zhe Liu ◽  
Jianxi Huang ◽  
...  
Keyword(s):  
Plant Height ◽  
Unmanned Aerial Vehicle ◽  
Estimation Error ◽  
Surface Model ◽  
Lodging Resistance ◽  
Height Estimation ◽  
Area Estimation ◽  
Corn Breeding ◽  
Aerial Vehicle ◽  
Uav Images

Phenotyping provides important support for corn breeding. Unfortunately, the rapid detection of phenotypes has been the major limiting factor in estimating and predicting the outcomes of breeding programs. This study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and deepening UAV techniques for comparing phenotypes and screening new corn varieties. Two geometric traits (plant height, canopy leaf area index (LAI)) and one lodging resistance trait (lodging area) were estimated in this study. It was found that stereoscopic and photogrammetric methods were promising ways to calculate a digital surface model (DSM) for estimating corn plant height from UAV images, with R2 = 0.7833 (p < 0.001) and a root mean square error (RMSE) = 0.1677. In addition to a height estimation, the height variation was analyzed for depicting and validating the corn canopy uniformity stability for different varieties. For the lodging area estimation, the normalized DSM (nDSM) method was more promising than the gray-level co-occurrence matrix (GLCM) textural features method. The estimation error using the nDSM ranged from 0.8% to 5.3%, and the estimation error using the GLCM ranged from 10.0% to 16.2%. Associations between the height estimation and lodging area estimation were done to find the corn varieties with optimal plant heights and lodging resistance. For the LAI estimation, the physical radiative transfer PROSAIL model offered both an accurate and robust estimation performance both at the middle (R2 = 0.7490, RMSE = 0.3443) and later growing stages (R2 = 0.7450, RMSE = 0.3154). What was more exciting was that the estimated sequential time series LAIs revealed a corn variety with poor resistance to lodging in a study area of Baogaofeng Farm. Overall, UAVs appear to provide a promising method to support phenotyping for crop breeding, and the phenotyping of corn breeding in this study validated this application.

scholarly journals Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4092 ◽  
Author(s):  
Xiongzhe Han ◽  
J. Alex Thomasson ◽  
G. Cody Bagnall ◽  
N. Ace Pugh ◽  
David W. Horne ◽  
...  
Keyword(s):  
Plant Height ◽  
Ground Truth ◽  
Ground Level ◽  
Strong Relationship ◽  
Future Application ◽  
Ground Control Points ◽  
Global Demand ◽  
Sorghum Plant ◽  
Aerial Vehicle ◽  
Uav Images

Continuing population growth will result in increasing global demand for food and fiber for the foreseeable future. During the growing season, variability in the height of crops provides important information on plant health, growth, and response to environmental effects. This paper indicates the feasibility of using structure from motion (SfM) on images collected from 120 m above ground level (AGL) with a fixed-wing unmanned aerial vehicle (UAV) to estimate sorghum plant height with reasonable accuracy on a relatively large farm field. Correlations between UAV-based estimates and ground truth were strong on all dates (R2 > 0.80) but are clearly better on some dates than others. Furthermore, a new method for improving UAV-based plant height estimates with multi-level ground control points (GCPs) was found to lower the root mean square error (RMSE) by about 20%. These results indicate that GCP-based height calibration has a potential for future application where accuracy is particularly important. Lastly, the image blur appeared to have a significant impact on the accuracy of plant height estimation. A strong correlation (R2 = 0.85) was observed between image quality and plant height RMSE and the influence of wind was a challenge in obtaining high-quality plant height data. A strong relationship (R2 = 0.99) existed between wind speed and image blurriness.

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