Fast Phenomics in Vineyards: Development of GRover, the Grapevine Rover, and LiDAR for Assessing Grapevine Traits in the Field
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the GRover’s Platform
2.2. LiDAR Sensor Specifications
2.3. Scans and Voxelization
2.4. Data Processing
2.5. Vineyards
- -
- Alverstoke teaching vineyard, University of Adelaide, Waite Campus, Adelaide, South Australia. Variety Shiraz, trained to a single cordon and spur or minimally pruned. A single foliage wire was used to vertically shoot position the vines during the growing season. This has a mildly sloping terrain with a winter mid-row cover crop;
- -
- South Australian Research and Development Institute (SARDI) research vineyard at Nuriootpa, South Australia. Variety Shiraz, trained to a single cordon and spur pruned. Vines were allowed to ”sprawl” during the growing season. Flat terrain. One planting of the Shiraz variety at this location was used for comparison of LiDAR scans with cordon and trunk volume measurements and another used for comparison with pruning weight measurements. Measurements were made in winter 2015.
3. Results and Discussion
3.1. Prototype Testing of Platform Robustness and Scanning Speed
3.2. Workflow Protocol Refinement: Plot Selection and Scan Cleaning Using Two Filters
- (1)
- Encoder dislodgement: Debris caught in the wheel sometimes dislodged the encoder, stopping it from spinning and tracking distance. This compressed the scan into two dimensions. There is no easy computational way of solving this problem, so the encoder was monitored during scans;
- (2)
- Light intensity: Although the LMS-400 gives precise spatial and reflectance data at a high rate, it is designed to operate below 2000 Lx and not under high light conditions. Lx values in indirect sunlight commonly range between 1000 Lx on an overcast day to 130,000 Lx in direct sunlight [3]. High light levels are the cause of the spurious, low-intensity blue points seen between the LiDAR and the vines in Figure 2b. However, the erroneous measurements are all low reflectance values and can be removed by filtering the scan based on a set reflectance value. Points with reflectance values were removed from the scan using the “filter points by value” plugin in CloudCompare (Figure 2b,c). In Figure 2b, there are 1.49 million total points, and 28% of those points were . The reflectance threshold was chosen qualitatively and removed spurious points without significantly affecting the biological interpretation of the scan. Green leaf material had a reflectance value between one and five;
- (3)
- Edge scattering: As with any LiDAR scan, there was scattering at the edges of objects, where light is reflected in unpredictable ways. An example of edge scattering can be seen between the main vine cordon in Figure 2b. The nearest neighbor statistical outlier plugin in CloudCompare [27] removed sparse outliers based on the distance of an individual point from its neighbors. By applying these filters, point-clouds were reduced to only the scanned objects. In Figure 2c, there are 1.12 million points. After the filter removing statistical outliers is applied, there are 1.04 million points in the final point cloud (Figure 2d) on which any computational analysis would be performed.
3.3. LiDAR is Able to Capture Vine Size and Structure at All Growth Stages
3.4. Preliminary Computational Analysis of LiDAR Scans by Voxelization Correlate with Measurements of Shiraz Trunk and Cordon Volume and Pruning Weights
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Recursion Level (R) | 6 | 7 | 8 | 9 | 10 | 11 |
---|---|---|---|---|---|---|
R2 (Pruning weight) | 0.088 | 0.47 | 0.71 | 0.86 | 0.92 | 0.78 |
R2 (Trunk & Cordon volume) | 0.25 | 0.59 | 0.65 | 0.73 | 0.73 | 0.72 |
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Siebers, M.H.; Edwards, E.J.; Jimenez-Berni, J.A.; Thomas, M.R.; Salim, M.; Walker, R.R. Fast Phenomics in Vineyards: Development of GRover, the Grapevine Rover, and LiDAR for Assessing Grapevine Traits in the Field. Sensors 2018, 18, 2924. https://doi.org/10.3390/s18092924
Siebers MH, Edwards EJ, Jimenez-Berni JA, Thomas MR, Salim M, Walker RR. Fast Phenomics in Vineyards: Development of GRover, the Grapevine Rover, and LiDAR for Assessing Grapevine Traits in the Field. Sensors. 2018; 18(9):2924. https://doi.org/10.3390/s18092924
Chicago/Turabian StyleSiebers, Matthew H., Everard J. Edwards, Jose A. Jimenez-Berni, Mark R. Thomas, Michael Salim, and Rob R. Walker. 2018. "Fast Phenomics in Vineyards: Development of GRover, the Grapevine Rover, and LiDAR for Assessing Grapevine Traits in the Field" Sensors 18, no. 9: 2924. https://doi.org/10.3390/s18092924