Pear Flower Cluster Quantification Using RGB Drone Imagery
Abstract
:1. Introduction
- (i)
- Examine if the UAV-based top-view is representative for the flower clusters present in the entire tree.
- (ii)
- Evaluation of the pixel-based classification algorithm for segmentation of the flower pixels from the background pixels.
- (iii)
- Evaluation of density-based clustering to group flower pixels for estimating the number of flower clusters.
- (iv)
- Comparison of the accuracy of the 2D- and 3D-based pixel and object-based methods for flower cluster quantification.
- (v)
- Comparison of the accuracy of all methods on individually segmented trees (tree-level) versus per three segmented trees (plot-level) to evaluate the importance and difficulty of tree delineation in the orchard environment.
2. Materials and Methods
2.1. Study Area and Flower Cluster Reference Data
2.2. RGB Drone Data Acquisition
2.2.1. RGB Drone Data for Flower Cluster Estimation
2.2.2. RGB Drone Data for Training the Pixel-Based Classifier
2.3. Data Processing
2.3.1. Colored Dense Point Cloud, Digital Elevation Model and Orthomosaic Generation
2.3.2. Pixel-Based Flower Classification
2.3.3. Flower Pixel Clustering
2.4. Magnitude of the Flower Occlusion Problem
2.5. Accuracy Assessment
2.5.1. Pixel-Based Classification
2.5.2. Optimization of DBSCAN Parameters
2.5.3. Flower Cluster Estimation Methods
3. Results
3.1. Magnitude of the Flower Occlusion Problem
3.2. Accuracy of the Pixel-Based Classification
3.3. Optimalisation of DBSCAN Parameters
3.4. Flower Cluster Estimation Models
3.4.1. Individual Tree Level
3.4.2. Plot or Multiple Tree Level
4. Discussion
4.1. Viewing Perspective
4.2. Tree Delineation
4.3. Pixel-Based Versus Object-Based Classification
4.4. Limitations and Recommendations for Future Research
5. Conclusions
- (i)
- The top-view perspective for flower cluster estimations can suffice if the trees have an open tree architecture but fail if the canopies have a lot of flower occlusion due to flower overlap.
- (ii)
- The object-based flower cluster estimation model based on colored dense point clouds gives the most accurate results for the flower cluster estimations for both orchards.
- (iii)
- It is better to work on plot level (i.e., multiple tree level) than on tree level for reducing the estimation error caused by errors in delineation of individual trees.
- (iv)
- In future research flower clusters should be counted per running meter instead of per tree to reduce errors in the ground truth and errors due to overlapping branches from neighboring trees.
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
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K | ε | R2 | RMSE | RRMSE | |
---|---|---|---|---|---|
2D Object-based tree | 1 | 0.005 | 0.72 (0.10) | 15 (2) | 14% (2%) |
2D Object-based plot | 2 | 0.012 | 0.81 (0.14) | 34 (8) | 10% (2%) |
3D Object-based tree | 12 | 0.11 | 0.44 (0.15) | 33 (8) | 23% (6%) |
3D Object-based plot | 16 | 0.135 | 0.71 (0.2) | 60 (16) | 14% (4 %) |
K | ε | R2 | RMSE | RRMSE | |
---|---|---|---|---|---|
2D Object-based tree | 9 | 0.019 | 0.60 (0.16) | 19 (4) | 16% (3%) |
2D Object-based plot | 9 | 0.022 | 0.87 (0.10) | 32 (9) | 9% (3%) |
3D Object-based tree | 1 | 0.06 | 0.45 (0.17) | 33 (6) | 23% (4%) |
3D Object-based plot | 13 | 0.12 | 0.70 (0.21) | 64 (18) | 15%(4%) |
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Vanbrabant, Y.; Delalieux, S.; Tits, L.; Pauly, K.; Vandermaesen, J.; Somers, B. Pear Flower Cluster Quantification Using RGB Drone Imagery. Agronomy 2020, 10, 407. https://doi.org/10.3390/agronomy10030407
Vanbrabant Y, Delalieux S, Tits L, Pauly K, Vandermaesen J, Somers B. Pear Flower Cluster Quantification Using RGB Drone Imagery. Agronomy. 2020; 10(3):407. https://doi.org/10.3390/agronomy10030407
Chicago/Turabian StyleVanbrabant, Yasmin, Stephanie Delalieux, Laurent Tits, Klaas Pauly, Joke Vandermaesen, and Ben Somers. 2020. "Pear Flower Cluster Quantification Using RGB Drone Imagery" Agronomy 10, no. 3: 407. https://doi.org/10.3390/agronomy10030407