Measurement and Calibration of Plant-Height from Fixed-Wing 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 fe...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4092 |
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| author | Xiongzhe Han J. Alex Thomasson G. Cody Bagnall N. Ace Pugh David W. Horne William L. Rooney Jinha Jung Anjin Chang Lonesome Malambo Sorin C. Popescu Ian T. Gates Dale A. Cope |
| author_facet | Xiongzhe Han J. Alex Thomasson G. Cody Bagnall N. Ace Pugh David W. Horne William L. Rooney Jinha Jung Anjin Chang Lonesome Malambo Sorin C. Popescu Ian T. Gates Dale A. Cope |
| author_sort | Xiongzhe Han |
| collection | DOAJ |
| description | 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 (R<sup>2</sup> > 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 (R<sup>2</sup> = 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 (R<sup>2</sup> = 0.99) existed between wind speed and image blurriness. |
| first_indexed | 2024-04-13T00:45:06Z |
| format | Article |
| id | doaj.art-10c10775975d4a5dbfb4a672e37dfa5f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:45:06Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-10c10775975d4a5dbfb4a672e37dfa5f2022-12-22T03:10:02ZengMDPI AGSensors1424-82202018-11-011812409210.3390/s18124092s18124092Measurement and Calibration of Plant-Height from Fixed-Wing UAV ImagesXiongzhe Han0J. Alex Thomasson1G. Cody Bagnall2N. Ace Pugh3David W. Horne4William L. Rooney5Jinha Jung6Anjin Chang7Lonesome Malambo8Sorin C. Popescu9Ian T. Gates10Dale A. Cope11Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USADepartment of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USADepartment of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USADepartment of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USADepartment of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USADepartment of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USASchool of Engineering and Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USASchool of Engineering and Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USADepartment of Ecosystem Science & Management, Texas A&M University, College Station, TX 77843, USADepartment of Ecosystem Science & Management, Texas A&M University, College Station, TX 77843, USANatural Resources Institute, Texas A&M University, College Station, TX 77843, USADepartment of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USAContinuing 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 (R<sup>2</sup> > 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 (R<sup>2</sup> = 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 (R<sup>2</sup> = 0.99) existed between wind speed and image blurriness.https://www.mdpi.com/1424-8220/18/12/4092fixed-wing UAVsorghum plant heightstructure from motionmulti-level GCPsGCP-based height calibrationimage blurrinesswind speed |
| spellingShingle | Xiongzhe Han J. Alex Thomasson G. Cody Bagnall N. Ace Pugh David W. Horne William L. Rooney Jinha Jung Anjin Chang Lonesome Malambo Sorin C. Popescu Ian T. Gates Dale A. Cope Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images Sensors fixed-wing UAV sorghum plant height structure from motion multi-level GCPs GCP-based height calibration image blurriness wind speed |
| title | Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images |
| title_full | Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images |
| title_fullStr | Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images |
| title_full_unstemmed | Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images |
| title_short | Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images |
| title_sort | measurement and calibration of plant height from fixed wing uav images |
| topic | fixed-wing UAV sorghum plant height structure from motion multi-level GCPs GCP-based height calibration image blurriness wind speed |
| url | https://www.mdpi.com/1424-8220/18/12/4092 |
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