Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat
Abstract
1. Introduction
2. Materials and Methods
2.1. Overview of the Experimental Area and Experimental Design
2.2. Data Handling
2.3. Image Preprocessing
2.4. Canopy Image Segmentation
2.4.1. Introduction to Image Algorithms
Random Forest (RF)
Support Vector Machine (SVM)
2.4.2. Image Segmentation Algorithm Specific Steps
Key Feature Extraction
Construction of Training Dataset
Classifier Construction and Biplot Generation
2.5. Vegetation Index Selection
2.6. Model Building
2.6.1. Random Forests
2.6.2. Ridge Regression
2.6.3. K-Nearest Neighbor
2.6.4. Support Vector Regression
2.6.5. Stacking Model
2.6.6. Voting Model
2.7. Modeling Evaluation
3. Results and Analysis
3.1. Variable Screening and Statistical Analysis
3.1.1. Descriptive Statistical Analysis
3.1.2. Z-Score Outlier Elimination
3.2. Analysis of Nitrogen Content in Winter Wheat Leaves at Different Periods
3.3. Correlation Between Vegetation Index and Nitrogen Content in Leaves
3.4. Estimation of Leaf Nitrogen Content Based on Vegetation Indices
3.5. Model Construction and Inversion of Leaf Nitrogen Content Estimation Based on Vegetation Indices
4. Discussion
4.1. The Application Value of Multispectral Remote Sensing Images in Crop Canopy Segmentation
4.2. Performance Analysis of Winter Wheat Leaf Nitrogen Content Monitoring Using Vegetation Indices
4.3. Advantages of Estimating Nitrogen Content of Winter Wheat Leaves Based on Integrated Learning Models
4.4. Feasibility and Limitations of UAV-Based Multispectral Estimation for Winter Wheat LNC
5. Research Significance and Application Prospects
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
CART | Classification and Regression Tree |
CIred-edge | Chlorophyll Index Red-Edge |
EVI | Enhanced Vegetation Index |
GNDVI | Green Normalized Difference Vegetation Index |
GRVI | Green-Red Vegetation Index |
K-NN | K-Nearest Neighbors |
LNC | Leaf Nitrogen Content |
MSAVI | Modified Soil-Adjusted Vegetation Index |
NDRE | Normalized Difference Red Edge Index |
NDVI | Normalized Difference Vegetation Index |
NIR | Near-Infrared |
OLS | Ordinary Least Squares |
PMS | Percentage of Missing Segmentation |
PWS | Percentage of Wrong Split |
RBF | Radial Basis Function |
RDVI | Relative Difference Vegetation Index |
RF | Random Forest |
ROI | Support Vector Machine |
RR | Ridge Regression |
SAVI | Soil-Adjusted Vegetation Index |
SIPI | Structure Insensitive Pigment Index |
SVM | Support Vector Machine |
SVR | Support Vector Regression |
UAV | Unmanned Aerial Vehicle |
VIs | Vegetation Indices |
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Treatment | Fertilizer Rate (kg/hm2) | ||
---|---|---|---|
N | P2O5 | K2O | |
N0 | 0 | 120 | 120 |
N1 | 100 | 120 | 120 |
N2 | 200 | 120 | 120 |
Model | Threshold | Accuracy/% | PMS/% | PWS/% |
---|---|---|---|---|
RF | NDVI | 90.11 | 12.18 | 31.66 |
EVI | 85.63 | 26.93 | 48.29 | |
MSAVI | 87.45 | 22.87 | 29.65 | |
SVM | NDVI | 86.29 | 17.28 | 57.96 |
EVI | 81.79 | 28.34 | 50.42 | |
MSAVI | 82.06 | 25.37 | 18.58 |
Type of Vegetation Index | Calculation Formula | Source |
---|---|---|
NDVI | [26] | |
GRVI | [27] | |
SAVI | [28] | |
GNDVI | [29] | |
SIPI | [30] | |
NDRE | [31] | |
RDVI | [32] | |
EVI | [33] | |
CIred-edge | [34] |
Period of Fertility | Datasets | Sample | Average | Kurtosis | Skewness | Minimum | Maximum |
---|---|---|---|---|---|---|---|
Jointing stage | Total | 195 | 32.870 | 0.624 | 0.357 | 22.174 | 43.278 |
Train | 137 | 33.212 | 0.603 | 0.581 | 23.145 | 43.278 | |
Test | 58 | 33.672 | 0.722 | 0.492 | 22.174 | 41.150 | |
Heading stage | Total | 195 | 36.676 | 0.535 | 0.367 | 30.170 | 45.101 |
Train | 137 | 38.302 | 0.501 | 0.604 | 34.332 | 45.101 | |
Test | 58 | 32.832 | 0.493 | 0.680 | 30.170 | 34.327 | |
Pre-grouting stage | Total | 195 | 30.692 | 0.706 | 0.272 | 25.065 | 36.914 |
Train | 137 | 32.100 | 0.875 | 0.491 | 29.038 | 36.914 | |
Test | 58 | 27.361 | 1.102 | 0.309 | 25.065 | 28.988 | |
Late grouting stage | Total | 195 | 22.392 | 0.651 | 0.426 | 15.122 | 31.661 |
Train | 137 | 24.447 | 0.812 | 0.646 | 20.148 | 31.661 | |
Test | 58 | 17.531 | 1.186 | 0.583 | 15.122 | 20.100 |
Model | Period of Fertility | R2 | RMSE | MAE |
---|---|---|---|---|
SVR | Jointing | 0.37 | 4.10 | 3.88 |
Heading | 0.48 | 3.34 | 3.02 | |
Pre-grouting | 0.43 | 3.69 | 3.45 | |
Late grouting | 0.33 | 4.25 | 3.94 | |
RF | Jointing | 0.58 | 2.97 | 2.76 |
Heading | 0.72 | 2.08 | 1.73 | |
Pre-grouting | 0.66 | 2.21 | 1.99 | |
Late grouting | 0.68 | 2.14 | 1.91 | |
RR | Jointing | 0.41 | 3.62 | 3.49 |
Heading | 0.52 | 3.15 | 3.10 | |
Pre-grouting | 0.47 | 3.47 | 3.13 | |
Late grouting | 0.40 | 3.67 | 3.42 | |
K-NN | Jointing | 051 | 3.20 | 3.23 |
Heading | 0.66 | 2.13 | 1.86 | |
Pre-grouting | 0.65 | 2.19 | 1.98 | |
Late grouting | 0.63 | 2.39 | 2.01 | |
Voting | Jointing | 0.69 | 1.98 | 1.80 |
Heading | 0.76 | 1.88 | 1.26 | |
Pre-grouting | 0.82 | 1.64 | 1.49 | |
Late grouting | 0.58 | 2.79 | 2.51 | |
Stacking | Jointing | 0.65 | 2.16 | 1.83 |
Heading | 0.73 | 2.05 | 1.77 | |
Pre-grouting | 0.80 | 1.77 | 1.15 | |
Late grouting | 0.71 | 2.18 | 1.94 |
Project/Indicator | UAV-Based Multispectral Method | Traditional Manual Sampling Method |
---|---|---|
Platform and sensors | DJI Phantom 4 Pro + RedEdge-MX | Manual sampling + laboratory chemical element analysis |
Single job duration (this study area) | 5 min | 5–6 days |
Data acquisition cost (estimated) | 150,000 RMB (unrestricted use) | 4000 RMB (single-use cost in this study, including sampling, labor, reagents, and elemental analysis) |
Spatial resolution | 8 cm/pixel | None |
Sampling method | Non-destructive sampling | Destructive sampling |
Testing frequency | Every 7 days or more frequently | Long sampling and elemental analysis cycle |
Data analysis and processing | Model-based prediction with near real-time output | Laboratory testing with longer turnaround time |
Applicability | Scalable to large areas, multiple time points, and different regions | Limited to small-scale or controlled experiments |
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Han, Y.; Zhang, J.; Bai, Y.; Liang, Z.; Guo, X.; Zhao, Y.; Feng, M.; Xiao, L.; Song, X.; Zhang, M.; et al. Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat. Agronomy 2025, 15, 1621. https://doi.org/10.3390/agronomy15071621
Han Y, Zhang J, Bai Y, Liang Z, Guo X, Zhao Y, Feng M, Xiao L, Song X, Zhang M, et al. Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat. Agronomy. 2025; 15(7):1621. https://doi.org/10.3390/agronomy15071621
Chicago/Turabian StyleHan, Yu, Jiaxue Zhang, Yan Bai, Zihao Liang, Xinhui Guo, Yu Zhao, Meichen Feng, Lujie Xiao, Xiaoyan Song, Meijun Zhang, and et al. 2025. "Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat" Agronomy 15, no. 7: 1621. https://doi.org/10.3390/agronomy15071621
APA StyleHan, Y., Zhang, J., Bai, Y., Liang, Z., Guo, X., Zhao, Y., Feng, M., Xiao, L., Song, X., Zhang, M., Yang, W., Li, G., Yang, S., Qiao, X., & Wang, C. (2025). Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat. Agronomy, 15(7), 1621. https://doi.org/10.3390/agronomy15071621