Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery
<p>Automated framework for crop mapping and canopy characterization.</p> "> Figure 2
<p>A Voronoi diagram delineating points acquired from [<a href="#B31-remotesensing-16-02679" class="html-bibr">31</a>].</p> "> Figure 3
<p>A map of the estimated NDVI, GCC and ExG indices.</p> "> Figure 4
<p>Orthophoto of study area.</p> "> Figure 5
<p>(<b>a</b>) Randomly selected areas of the study site. (<b>b</b>) Spectral profile of randomly selected areas of the study area.</p> "> Figure 6
<p>(<b>a</b>) Initially generated CHM. (<b>b</b>) Filtered CHM. (<b>c</b>) Local peak height. The unit of measurement for the CHM, filtered CHM, and generated local peak height maps is feet.</p> "> Figure 7
<p>Local maximal results showing the peak height of each crop.</p> "> Figure 8
<p>A Voronoi map showing the canopy sizes and the individual crop peak heights with noise.</p> "> Figure 9
<p>Refined crop canopy sizes.</p> "> Figure 10
<p>The refinement process of each crop canopy size from (<b>a</b>) ExG to (<b>b</b>) segmented ExG. (<b>c</b>) Reclassified segmented ExG. (<b>d</b>) Conversion of segmented ExG raster to polygon. (<b>e</b>) Soil feature selection. (<b>f</b>) Overlay of modified Voronoi on the RGB image.</p> "> Figure 11
<p>Influence of canopy refinement.</p> "> Figure 12
<p>Predictive ability of SVM with RBF kernel, Polynomial Regression with Degree 2 models, and Gradient Boosting Machine. The red line represents perfect predictions. The closer the points are to this line, the more accurate the model’s predictions.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. UAV Data Collection
2.2. Canopy Height Model (CHM) Generation and Individual Plant Identification
2.3. Canopy Size Delineation and Refinement
2.4. Machine Learning Integration
2.5. Evaluation Metrics
3. Implementations and Results
3.1. Study Area and Data Collection
3.2. CHM Development and Crop Detection
3.3. Canopy Size Estimation and Refinement
3.4. Statistical Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Initial Canopy Size (ft) | Refined Canopy Size (ft) | |
---|---|---|
Count | 13,000 | 13,000 |
Mean | 8.927187 | 5.781796 |
Standard deviation | 13.858262 | 12.339383 |
Minimum value | 0.901887 | 0.004016 |
Maximum value | 348.572489 | 348.021710 |
25% | 4.666112 | 2.715247 |
50% | 6.177899 | 3.914694 |
75% | 8.341895 | 5.359943 |
Model | R-Squared (%) | Mean Absolute Error (MAE) |
---|---|---|
Polynomial Regression (Degree 2) | 11 | 2.036 |
SVM (with RBF kernel) | 7 | 2.033 |
Gradient Boosting Machine | 10 | 2.044 |
Ridge | 9.6 | 2.058 |
Linear Regression | 9.6 | 2.058 |
OLS | 7.5 | 2.058 |
Lasso | 0.7 | 2.133 |
K-Nearest Neighbors | −4.0 | 2.197 |
Random Forest | −9.0 | 2.232 |
Decision tree | −78 | 2.940 |
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Dorbu, F.; Hashemi-Beni, L. Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery. Remote Sens. 2024, 16, 2679. https://doi.org/10.3390/rs16142679
Dorbu F, Hashemi-Beni L. Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery. Remote Sensing. 2024; 16(14):2679. https://doi.org/10.3390/rs16142679
Chicago/Turabian StyleDorbu, Freda, and Leila Hashemi-Beni. 2024. "Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery" Remote Sensing 16, no. 14: 2679. https://doi.org/10.3390/rs16142679