Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images †
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Image Collection from UAS Platforms
2.3. Field Data Collection
- (1)
- Plant stand counts. This was necessary to calculate the lodging rate. Stand counts are not constant within fields because of germination differences across varieties and field positions.
- (2)
- Number of lodged maize plants. Any maize stalks that had laid over due to environmental factors with an approximate inclination of 60° from a vertical position and would not likely be processed by the combine were deemed as lodging plants, including both root and stalk lodging.
2.4. Canopy Height Model Generation
2.5. Plant Height Extraction from DSM
- (1)
- Determination of the centerline on the georeferenced orthoimage or CHM raster for each row. In this step, two endpoints for each of the crop rows are manually selected and the centerline is drawn along the row. As demonstrated in Figure 4, the cyan solid line on each row represents the centerline. The lengths of the row centerlines may slightly differ, and a centerline is supposed to reasonably cover an entire row.
- (2)
- Drawing row polygons according to the centerlines. The polygons are regular rectangles with the long edges being the centerlines while the short edges being adjustable. Specifically, a centerline width of 10 cm was determined to filter out as many pixels as possible representing soil and lower leaves.
- (3)
- Computation of height information. Height is estimated on a per-row (or per-plot) basis using CHM values in the polygons painted with stripes along the row centerlines, as illustrated in Figure 4.
2.6. Lodging Detection Method
THEN non-lodging
ELSE
THEN lodging
3. Results and Discussion
3.1. Plant Height Validation
3.2. Lodging and Non-lodging Comparison over the Growing Season
3.3. Lodging Assessment
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Canon PowerShot S110 | GoPro HERO3+ Black Edition | DJI FC330 | DJI FC200 | |
---|---|---|---|---|
UAS platform | senseFly eBee fixed-wing | 3DR Solo quadcopter | DJI Phantom 4 quadcopter | DJI Phantom 2 Vision+ quadcopter |
Camera band | R-G-NIR * | R-G-B | R-G-B | R-G-B |
Lens type | Perspective | Fisheye | Perspective | Fisheye |
Array (pixels) | 4048 × 3048 | 4000 × 3000 | 4000 × 3000 | 4608 × 3456 |
Sensor size (mm × mm) | 7.4 × 5.6 | 6.2 × 4.7 | 6.3 × 4.7 | 6.2 × 4.6 |
Focal length (mm) ** | 24 | 15 | 20 | 30 |
Exposure time (sec) | 1/2000 | Auto | Auto | Auto |
F-stop *** | f/2 | f/2.8 | f/2.8 | f/2.8 |
ISO | 80 | 100 | 100 | 100 |
Image format | TIFF | JPEG | JPEG | JPEG |
Flight Date | Platform | Sensor Type | Flight Height (m) | Image Number Taken | GSD (mm/Pixel) | Point Density (Points/m2) |
---|---|---|---|---|---|---|
12 April 2016 | DJI Phantom 2 Vision+ | RGB | 40 | 84 | 17.1 | 1548.9 |
22 April 2016 | DJI Phantom 4 | RGB | 50 | 89 | 21.8 | 649.6 |
27 April 2016 | DJI Phantom 4 | RGB | 50 | 84 | 23.3 | 764.5 |
6 May 2016 | DJI Phantom 4 | RGB | 30 | 540 | 13.7 | 1333.4 |
17 May 2016 | DJI Phantom 4 | RGB | 40 | 276 | 19.1 | 686.1 |
20 May 2016 | DJI Phantom 4 | RGB | 30 | 418 | 13.5 | 3145.1 |
26 May 2016 | eBee | NIR | 101 | 179 | 38.4 | 81.6 |
31 May 2016 | DJI Phantom 4 | RGB | 40 | 414 | 19.0 | 1545.1 |
8 June 2016 | 3DR Solo | RGB | 30 | 691 | 20.0 | 865.1 |
9 June 2016 | eBee | NIR | 101 | 178 | 40.7 | 76.2 |
10 June 2016 | 3DR Solo | RGB | 30 | 674 | 20.7 | 748.8 |
14 June 2016 | 3DR Solo | RGB | 30 | 685 | 21.4 | 869.2 |
23 June 2016 | 3DR Solo | RGB | 30 | 674 | 19.7 | 868.1 |
23 June 2016 | eBee | NIR | 101 | 200 | 39.4 | 59.6 |
28 June 2016 | 3DR Solo | RGB | 30 | 657 | 21.0 | 794.9 |
30 June 2016 | DJI Phantom 4 | RGB | 20 | 656 | 7.7 | 5995.1 |
12 July 2016 | 3DR Solo | RGB | 30 | 676 | 19.3 | 989.5 |
13 July 2016 | DJI Phantom 4 | RGB | 20 | 585 | 10.2 | 1540.3 |
15 July 2016 | eBee | NIR | 101 | 168 | 40.1 | 51.2 |
Dot Color in Figure 6 | Date Ground Height Collected | Flight Experiment Date Closest to the Ground Data Collection Date | UAS Platform | Portion of the Field Trial Included |
---|---|---|---|---|
Magenta | 26 April 2016 | 27 April 2016 | DJI Phantom 4 | Upper |
Cyan | 6 May 2016 | 6 May 2016 | DJI Phantom 4 | Lower |
Green | 13 May 2016 | 17 May 2016 | DJI Phantom 4 | Upper and lower |
Blue | 27 May 2016 | 26 May 2016 | eBee | Upper and lower |
Black | 6 June 2016 | 8 June 2016 | 3DR Solo | Upper and lower |
Yellow | 1 July 2016 | 30 June 2016 | DJI Phantom 4 | Upper and lower |
GLR | Hmin | Hmax | Hmean | Hstd | H50 | H80 | H99 | Herr | Hcv | Yield | ULR | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
GLR | 1.00 | |||||||||||
Hmin | −0.05 | 1.00 | ||||||||||
Hmax | −0.08 | 0.06 | 1.00 | |||||||||
Hmean | −0.59 * | 0.22 * | 0.29 * | 1.00 | ||||||||
Hstd | 0.33 * | −0.30 * | 0.33 * | −0.47 * | 1.00 | |||||||
H50 | −0.58 * | 0.17 * | 0.25 * | 0.96 * | −0.35 * | 1.00 | ||||||
H80 | −0.48 | 0.12 * | 0.52 * | 0.83 * | 0.04 | 0.82 * | 1.00 | |||||
H99 | −0.12 * | 0.08 | 0.86 * | 0.43 * | 0.31 * | 0.38 * | 0.68 * | 1.00 | ||||
Herr | −0.60 * | 0.14 * | −0.04 | 0.94 * | −0.58 * | 0.92 * | 0.70 * | 0.16 * | 1.00 | |||
Hcv | 0.64 * | −0.21 * | −0.08 ** | −0.92 * | 0.57 * | −0.90 * | −0.72 * | −0.19 * | −0.94 * | 1.00 | ||
Yield | −0.49 * | −0.53 * | 0.27 * | 0.62 * | −0.17 * | 0.60 * | 0.59 * | 0.36 * | 0.55 * | −0.54 * | 1.00 | |
ULR | 0.71 * | −0.11 | −0.14 * | −0.74 * | 0.44 * | −0.74 * | −0.60 * | −0.21 * | −0.75 * | 0.83 * | −0.50 * | 1.00 |
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Share and Cite
Chu, T.; Starek, M.J.; Brewer, M.J.; Murray, S.C.; Pruter, L.S. Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images. Remote Sens. 2017, 9, 923. https://doi.org/10.3390/rs9090923
Chu T, Starek MJ, Brewer MJ, Murray SC, Pruter LS. Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images. Remote Sensing. 2017; 9(9):923. https://doi.org/10.3390/rs9090923
Chicago/Turabian StyleChu, Tianxing, Michael J. Starek, Michael J. Brewer, Seth C. Murray, and Luke S. Pruter. 2017. "Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images" Remote Sensing 9, no. 9: 923. https://doi.org/10.3390/rs9090923