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. 2019 Jan 28;19(3):535.
doi: 10.3390/s19030535.

Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley

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Free PMC article

Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley

Victor P Rueda-Ayala et al. Sensors (Basel). .
Free PMC article

Abstract

Pastures are botanically diverse and difficult to characterize. Digital modeling of pasture biomass and quality by non-destructive methods can provide highly valuable support for decision-making. This study aimed to evaluate aerial and on-ground methods to characterize grass ley fields, estimating plant height, biomass and volume, using digital grass models. Two fields were sampled, one timothy-dominant and the other ryegrass-dominant. Both sensing systems allowed estimation of biomass, volume and plant height, which were compared with ground truth, also taking into consideration basic economical aspects. To obtain ground-truth data for validation, 10 plots of 1 m² were manually and destructively sampled on each field. The studied systems differed in data resolution, thus in estimation capability. There was a reasonably good agreement between the UAV-based, the RGB-D-based estimates and the manual height measurements on both fields. RGB-D-based estimation correlated well with ground truth of plant height ( R 2 > 0.80 ) for both fields, and with dry biomass ( R 2 = 0.88 ), only for the timothy field. RGB-D-based estimation of plant volume for ryegrass showed a high agreement ( R 2 = 0.87 ). The UAV-based system showed a weaker estimation capability for plant height and dry biomass ( R 2 < 0.6 ). UAV-systems are more affordable, easier to operate and can cover a larger surface. On-ground techniques with RGB-D cameras can produce highly detailed models, but with more variable results than UAV-based models. On-ground RGB-D data can be effectively analysed with open source software, which is a cost reduction advantage, compared with aerial image analysis. Since the resolution for agricultural operations does not need fine identification the end-details of the grass plants, the use of aerial platforms could result a better option in grasslands.

Keywords: 3D crop modeling; depth images; on-ground sensing; parameter acquisition; remote sensing.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Field test conducted at NIBIO Særheim, orthophoto of the ryegrass-dominant field (a) with 10 sampling plots and a zoomed in 1 m2 sampling plot subdivided in four quadrants (b); UAV sampling system (c) and RGB-D sampling system (d).
Figure 2
Figure 2
Section of the 3D reconstruction: before filtering (a); removed points are marked in fluorescent (b) and after filtering (c).
Figure 3
Figure 3
Point clouds created by RGB-D (Microsoft Kinect® v2) system.
Figure 4
Figure 4
Alpha shapes for the same point cloud using alpha = 0.1 (a) and (b), alpha = 0.2 (c) and alpha = 0.4 (d).
Figure 5
Figure 5
Model constructed by photogrammetry methods (a) and processes of point cloud of DSM model (b) and solid generation (c).
Figure 6
Figure 6
RGB-D estimated grass height compared with field measurements on all four quadrants per sampling plot (a), and raising plate-meter height per sampling plot (b), on fields 1 and 2. Shadow indicates upper and lower confidence limits.
Figure 7
Figure 7
RGB-D estimated grass height (a) and volume (b) compared with dry biomass per sampling plot, on field 1 and 2. Shadow indicates upper and lower confidence limits.
Figure 8
Figure 8
Actual plant height (raising plate-meter) averaged by plot compared with average dry biomass. Shadow indicates upper and lower confidence limits.
Figure 9
Figure 9
UAV estimated grass volume compared with measured dry biomass (a) and with raising plate-meter height (b) averaged per sampling plot, on field 1 and 2. Shadow indicates upper and lower confidence limits.
Figure 10
Figure 10
RGB-D estimated grass volume compared with raising plate-meter height averaged per sampling plot, on field 1 and 2. Shadow indicates upper and lower confidence limits.

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