Evaluating UAV-Based Remote Sensing for Hay Yield Estimation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Field Information
2.2. Ground Truth-Data Collection
2.3. UAV System and Remote Sensing Data Collection
2.4. Data Processing and Analysis
3. Results
3.1. Ground-Truth Data: Comparison and Mass
3.2. Model Performance
3.2.1. VI Correlation
3.2.2. RFE Results
3.2.3. Model Results Based on RFE
4. Discussion
4.1. Ground-Truth Data
4.2. Remote-Sensing Data
4.3. Biomass Estimation in a Mixed Hay Crop
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Index | Equation * | Features | Reference |
---|---|---|---|
Enhanced Vegetation Index (EVI) | - Correct NDVI results for air and soil influence - Appropriate for dense canopy area | [32] | |
Enhanced Vegetation Index 2 (EVI 2) | - Correct EVI results which is calculated by red and NIR bands | [33] | |
Green Normalized Difference Vegetation Index (GNDVI) | - More sensitive to chlorophyll concentration than NDVI | [34] | |
Simple Ratio (SR) | - Simple index to distinguish vegetation from soil | [35] | |
Modified Chlorophyll Absorption Ratio Index/Optimized Soil-Adjusted Vegetation Index red edge (MCARI/OSAVI red edge) ** | - Minimize the effect of non-photosynthetic materials on spectral estimates of absorbed photosynthetically active radiation | [36] | |
- Enhanced SAVI to minimize soil reflectance | |||
Normalized-Difference Red Edge Index (NDRE) | - Sensitive index to chlorophyll - Improve variation detection | [37] | |
Normalized Difference Vegetation Index (NDVI) | - Widespread index related to chlorophyll | [38] | |
Red–Green–Blue Vegetation Index (RGBVI) | - Related to chlorophyll - Enhanced index for green reflectance | [39] | |
Soil Adjusted Vegetation Index (SAVI) | - Minimize soil brightness influence - Appropriate for arid regions | [35] | |
Simplified Canopy Chlorophyll Content Index (SCCCI) | - Sensitive to N uptake and total plant N% - Appropriate to recognize growth stage | [40] | |
Transformed Chlorophyll Absorption in the Reflectance Index/Optimized Soil-Adjusted Vegetation Index red edge (TCARI/OSAVI red edge) *** | - Indicate the relative abundance of chlorophyll - Affected by underlying soil reflectance and leaf area index | [36] | |
- Enhanced SAVI to minimize soil reflectance |
Index | Equation * | Features |
---|---|---|
Contrast | - Measures the drastic change in gray level between contiguous pixels - High contrast images feature high spatial frequencies | |
Correlation | - Measures the linear dependency in the image - High correlation values imply a linear relationship between the gray levels of adjacent pixel pairs | |
Energy | - Measures texture uniformity or pixel pair repetitions - High energy occurs when the distribution of gray level values is constant or periodic | |
Entropy | - Measures the disorder of an image and is negatively correlated with Energy - Entropy is high when the image is texturally complex or includes much noise | |
Homogeneity | - Measures image homogeneity - Sensitive to the presence of near diagonal elements in a GLCM, representing the similarity in gray level between adjacent pixels |
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Rating | Proportion of Timothy Grass and Red Clover |
---|---|
1 | Mostly timothy grass |
2 | Approximately 2:1 timothy to red clover |
3 | Approximately equal |
4 | Approximately 1:2 timothy to red clover |
5 | Mostly red clover |
Group | Location | Date (2020) | Ground Sample Distance (m) | Expected Ground Resolution (mm pixel−1) | Image Resolution (pixels × pixels) | Flight Speed (km h−1) | Frames per Second (fps) |
---|---|---|---|---|---|---|---|
Plot | Plot 8 | 14 Sept. | 30 | 20 | 1280 × 960 | 7 | 1 |
Plot 16 | 14 Sept. | ||||||
Plot 29 | 14 Sept. | ||||||
Field | North | 14 Sept. | 50 | 34 | 1280 × 960 | 7 | 1 |
Middle | 15 Sept. | ||||||
South | 15 Sept. |
Group for ANOVA Test | Composition | Sample Proportion (%) | Number of Sampling Locations in Each Composition * | ||||||
---|---|---|---|---|---|---|---|---|---|
Plot 8 | Plot 16 | Plot 29 | N | M | S | ||||
All | Grass and clover | Mostly timothy grass | 20.0 | 3 | 2 | 2 | 1 | - | - |
Approximately 2:1 timothy to red clover | 17.5 | - | 1 | - | 2 | 2 | 2 | ||
Equal | 5.0 | 1 | - | - | 1 | - | - | ||
Approximately 1:2 timothy to red clover | 7.5 | - | - | - | 3 | - | - | ||
Mostly red clover | 20.0 | 3 | - | 1 | 3 | 1 | - | ||
Including other biomasses ** | Mostly grass and 2:1 grass to clover | 25.0 | - | 1 | 1 | - | 7 | 1 | |
Equal | 2.5 | - | - | - | - | 1 | - | ||
Mostly clover and 1:2 grass to clover | 2.5 | - | - | - | - | 1 | - |
Dry Mass (kg) | Number of Quadrat Locations |
---|---|
Over 0.5 | 5 |
0.4–0.5 | 7 |
0.3–0.4 | 16 |
0.2–0.3 | 9 |
0.1–0.2 | 2 |
Under 0.1 | 1 |
Number of Variables | RMSE (g m−2) | Recommended Significant Variables * | |||||||
---|---|---|---|---|---|---|---|---|---|
Mean | Median | Quantile 90% | Quantile 10% | ||||||
All | Only VI | All | Only VI | All | Only VI | All | Only VI | ||
1 | 115.2 | 116.1 | 112.2 | 112.4 | 106.3 | 106 | 115 | 121.6 | - |
2 | 115.2 | 114.8 | 111.9 | 112 | 106.3 | 106 | 114.8 | 118.6 | TCARI/OSAVI RE |
3 | 118.7 | 115.9 | 115.7 | 112.8 | 108.1 | 108 | 115.1 | 117.5 | MCARI/OSAVI RE |
4 | 120.3 | 117.9 | 118.6 | 114.1 | 111.3 | 109.9 | 114.9 | 117.8 | - |
5 | 121.4 | 119.6 | 119 | 116.5 | 114 | 111 | 114.2 | 116.7 | - |
6 | 121.9 | 121 | 119.4 | 118.7 | 114.6 | 112.2 | 114.5 | 116.8 | TCARI/OSAVI RE |
7 | 121.6 | 122.5 | 120 | 119.8 | 115.4 | 113.4 | 115.2 | 117.7 | MCARI/OSAVI RE |
8 | 120.9 | 122.2 | 120.3 | 120.9 | 115.4 | 113.1 | 115.6 | 117.2 | SAVI |
9 | 120.9 | 121.9 | 120.7 | 119.9 | 115.1 | 113.1 | 115.4 | 124.8 | RGBVI |
10 | 120.2 | 122.9 | 120 | 122.7 | 114.6 | 114.8 | 116.2 | 124.7 | EVI2 |
Optimum number of variables | 2 | 2 | 2 | 2 | 2 | 2 | 5 | 5 |
Number of Variables | R2 | RMSE (g m−2) | MAE (g m−2) | Optimum Number of Variables | Significant Variables |
---|---|---|---|---|---|
1 | 0.33 | 142.5 | 117.67 | Five | Contrast Correlation Energy Entropy Homogeneity |
2 | 0.34 | 137.1 | 113.43 | ||
3 | 0.29 | 134.5 | 110.61 | ||
4 | 0.31 | 128.5 | 104.78 | ||
5 | 0.38 | 117.9 | 96.18 |
Group | Statistics (Variables from RFE) | R2 | RMSE (%) | MAE (g m−2) | |||
---|---|---|---|---|---|---|---|
VIs | VIs and Texture Feature | VIs | VIs and Texture Feature | VIs | VIs and Texture Feature | ||
Plot | Quantile 90% * | 0.52 | 0.53 | 21.79 | 21.46 | 71.26 | 67.68 |
Quantile 10% ** | 0.68 | 0.68 | 17.84 | 17.72 | 54.49 | 56.44 | |
Mean * | 0.61 | 0.61 | 19.76 | 19.66 | 64.80 | 64.65 | |
Median * | 0.58 | 0.59 | 20.42 | 20.25 | 67.23 | 67.18 | |
Field | Quantile 90% * | 0.05 | 0.18 | 30.06 | 28.01 | 84.43 | 81.71 |
Quantile 10% ** | 0.12 | 0.31 | 28.90 | 25.71 | 84.35 | 77.53 | |
Mean * | 0.01 | 0.09 | 30.79 | 29.50 | 85.67 | 83.61 | |
Median * | 0.00 | 0.07 | 30.88 | 29.71 | 85.54 | 83.07 | |
Plot and field | Quantile 90% * | 0.21 | 0.28 | 29.20 | 27.97 | 82.02 | 79.31 |
Quantile 10% ** | 0.32 | 0.41 | 27.08 | 25.34 | 77.73 | 76.68 | |
Mean * | 0.20 | 0.25 | 29.46 | 28.45 | 82.76 | 80.93 | |
Median * | 0.16 | 0.21 | 30.12 | 29.29 | 83.37 | 82.19 |
Sensing Type | Crops and Purpose | Measurements and Accuracy | Limitations | Reference |
---|---|---|---|---|
Proximal and remote (ultrasonic sensors and hyperspectral sensors on UAV) | Grass (Lolio-Cynosuretum)—data fusion and estimation of yield | Plant height and hyperspectral reflectance—R2 = 0.8 | Lower accuracy caused by too-long or too-short sward heights | [23] |
Extremely heterogeneous grasslands such as are typical for very leniently stocked pastures | ||||
Proximal and remote (ultrasonic sensors and hyperspectral sensors on UAV) | Grass (Lolio-Cynosuretum)—data fusion and estimation of yield | Plant height and hyperspectral reflectance—R2 = 0.5 | Highly complex variation in pasture or sward structure | [26] |
Proximal (infrared and ultrasonic distance sensors) | Mixed grass, bermudagrass, hybrid pearl millet, and oats—estimating plant height and yield | Plant height—15 to 20% yield prediction error | Tolerance to withstand dust and dirt | [25] |
Proximal (ultrasonic sensors) | Grass—improving the accuracy of measuring plant height | Plant height—R2 = 0.8 | Tilting or bouncing of sensor array | [27] |
Remote (RGB camera on UAV) | Barley—simple estimation of yield | Plant height (crop surface model) and RGB reflectance—R2 = 0.8 | Inability to collect data at specific growth stages due to weather (e.g., clouds and rain) | [24] |
Ground Sample Distance (m) | Expected Ground Resolution (mm pixel−1) | Image Resolution (pixels × pixels) | Flight Speed (km h−1) | Frame per Second (fps) | Control Application |
---|---|---|---|---|---|
20 | 5 | 4864 × 3648 | 7 | 0.5 | Litchi |
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Lee, K.; Sudduth, K.A.; Zhou, J. Evaluating UAV-Based Remote Sensing for Hay Yield Estimation. Sensors 2024, 24, 5326. https://doi.org/10.3390/s24165326
Lee K, Sudduth KA, Zhou J. Evaluating UAV-Based Remote Sensing for Hay Yield Estimation. Sensors. 2024; 24(16):5326. https://doi.org/10.3390/s24165326
Chicago/Turabian StyleLee, Kyuho, Kenneth A. Sudduth, and Jianfeng Zhou. 2024. "Evaluating UAV-Based Remote Sensing for Hay Yield Estimation" Sensors 24, no. 16: 5326. https://doi.org/10.3390/s24165326