Development of Maize Canopy Architecture Indicators Through UAV Multi-Source Data
Abstract
1. Introduction
2. Materials and Methods
2.1. Experimental Site and Design
2.2. Data Acquisition
2.2.1. UAV Imagery Acquisition
2.2.2. LAI Measurement and Processing
2.2.3. Yield
2.3. Imagery Processing
2.4. Features Extraction
2.4.1. Vegetation Indices
2.4.2. Texture Indices
2.5. Construction of Deep Learning Model
2.6. Model Accuracy Evaluation
3. Results
3.1. Descriptive Statistics
3.2. Construction of Canopy Architecture Indicators
3.3. Feasibility Analysis of Kurtosis and Skewness
3.3.1. Correlation Analysis
3.3.2. Yield Estimation
3.4. Canopy Indicator Estimation
3.4.1. Correlation Between VIs and Kurtosis and Skewness
3.4.2. Kurtosis and Skewness Estimation Results
4. Discussion
4.1. Vertical Distribution of Leaf Area in Maize Canopy
4.2. Effect of Canopy Architecture Indicators on Photosynthesis and Yield
4.3. Effect of Planting Density on Photosynthesis and the Estimation Accuracy of Canopy Indicators
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
CC | Canopy coverage |
LAI | Leaf area index |
PH | Plant height |
LAD | Leaf area density |
MS | Multispectral |
LiDAR | Light detection and ranging |
UAV | Unmanned aerial vehicle |
M3M | Mavic 3 multispectral |
R1 | Silking stage |
R5 | Dent stage |
R2 | Coefficient of determination |
VIs | Vegetation indices |
TIs | Texture indices |
GLCM | Gray-level co-occurrence matrix |
RMSE | Root-mean-square error |
MAE | Mean absolute error |
References
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Vegetation Indices | Formula | References |
---|---|---|
CARI (chlorophyll absorption ratio index) | [24] | |
Cig (chlorophyll index—green) | [25] | |
Datt | [26] | |
DVI (difference vegetation index) | [27] | |
GDVI (green difference vegetation index) | [28] | |
MCARI (modified chlorophyll absorption in reflectance index) | [24] | |
MTVI (modified triangular vegetation index) | [29] | |
NDVI (normalized difference vegetation index) | [30] | |
GNDVI (green normalized difference vegetation index) | [31] | |
NDRE (normalized difference red-edge index) | [32] | |
OSAVI (optimized soil-adjusted vegetation index) | [33] | |
PSRI (plant senescence reflectance index) | [34] | |
RVI (ratio vegetation index) | [35] | |
TCARI (transformed chlorophyll absorption ratio) | [36] | |
TVI (triangular vegetation index) | [24] |
Texture Indices | Formula | References |
---|---|---|
Mean | [37] | |
Variance | [37] | |
Homogeneity | [37] | |
Contrast | [37] | |
Dissimilarity | [38] | |
Entropy | [38] | |
Second moment | [38] | |
Correlation | [38] |
Feature Type | Features | Kurtosis | Skewness | ||
---|---|---|---|---|---|
R1 Stage | R5 Stage | R1 Stage | R5 Stage | ||
VIs | CARI | 0.392 *** | 0.420 *** | 0.374 *** | 0.445 *** |
Cig | 0.383 *** | 0.117 | 0.461 *** | 0.259 *** | |
Datt | −0.125 | −0.360 *** | −0.307 *** | −0.233 *** | |
DVI | 0.389 *** | 0.543 *** | 0.195 ** | 0.476 *** | |
GDVI | 0.457 *** | 0.579 *** | 0.233 *** | 0.537 *** | |
MCARI | 0.255 *** | 0.441 *** | 0.336 *** | 0.562 *** | |
MTVI | −0.175 ** | 0.429 *** | 0.167 ** | −0.196 ** | |
NDVI | 0.425 *** | 0.487 *** | 0.130 * | 0.452 *** | |
GNDVI | 0.340 *** | 0.386 *** | 0.103 | 0.363 *** | |
NDRE | −0.210 ** | 0.493 *** | −0.146 * | 0.315 *** | |
OSAVI | 0.481 *** | 0.239 *** | 0.264 *** | 0.131 * | |
PSRI | 0.076 | −0.234 *** | 0.032 | −0.100 | |
RVI | 0.398 *** | 0.525 *** | 0.358 *** | 0.518 *** | |
TCARI | −0.332 *** | −0.468 *** | −0.220 *** | −0.341 *** | |
TVI | 0.437 *** | 0.353 *** | 0.404 *** | 0.502 *** | |
TIs | Mean | −0.130 | −0.078 | 0.152 * | −0.030 |
Variance | 0.348 *** | 0.431 *** | 0.194 ** | 0.271 *** | |
Homogeneity | −0.271 *** | −0.375 *** | −0.181 * | −0.298 *** | |
Contrast | 0.294 *** | 0.453 *** | 0.148 * | 0.417 *** | |
Dissimilarity | 0.261 *** | 0.359 *** | 0.128 | 0.115 | |
Entropy | −0.178 * | 0.301 *** | 0.370 *** | 0.108 | |
Second moment | 0.013 | −0.287 *** | 0.114 | −0.386 *** | |
Correlation | 0.385 *** | 0.229 ** | 0.312 *** | 0.277 *** |
Canopy Indicators | Features | R1 Stage | R5 Stage | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE | MAE | ||
Kurtosis | VI images | 0.447 | 0.055 | 0.043 | 0.554 | 0.176 | 0.140 |
TI images | 0.396 | 0.057 | 0.048 | 0.435 | 0.198 | 0.161 | |
Point clouds | 0.694 | 0.041 | 0.034 | 0.798 | 0.119 | 0.098 | |
All | 0.792 | 0.034 | 0.026 | 0.841 | 0.105 | 0.085 | |
Skewness | VI images | 0.431 | 0.043 | 0.034 | 0.532 | 0.105 | 0.083 |
TI images | 0.390 | 0.045 | 0.036 | 0.449 | 0.114 | 0.093 | |
Point clouds | 0.747 | 0.029 | 0.023 | 0.809 | 0.067 | 0.054 | |
All | 0.815 | 0.025 | 0.020 | 0.859 | 0.058 | 0.046 |
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Zhu, S.; Han, D.; Zhang, W.; Yang, T.; Yao, Z.; Liu, T.; Sun, C. Development of Maize Canopy Architecture Indicators Through UAV Multi-Source Data. Agronomy 2025, 15, 1991. https://doi.org/10.3390/agronomy15081991
Zhu S, Han D, Zhang W, Yang T, Yao Z, Liu T, Sun C. Development of Maize Canopy Architecture Indicators Through UAV Multi-Source Data. Agronomy. 2025; 15(8):1991. https://doi.org/10.3390/agronomy15081991
Chicago/Turabian StyleZhu, Shaolong, Dongwei Han, Weijun Zhang, Tianle Yang, Zhaosheng Yao, Tao Liu, and Chengming Sun. 2025. "Development of Maize Canopy Architecture Indicators Through UAV Multi-Source Data" Agronomy 15, no. 8: 1991. https://doi.org/10.3390/agronomy15081991
APA StyleZhu, S., Han, D., Zhang, W., Yang, T., Yao, Z., Liu, T., & Sun, C. (2025). Development of Maize Canopy Architecture Indicators Through UAV Multi-Source Data. Agronomy, 15(8), 1991. https://doi.org/10.3390/agronomy15081991