Estimation of Leaf, Spike, Stem and Total Biomass of Winter Wheat Under Water-Deficit Conditions Using UAV Multimodal Data and Machine Learning
Abstract
1. Introduction
2. Materials and Methods
2.1. Experimental Design
2.2. Data Acquisition
2.3. Image Processing
2.4. Calculation of the Leaf, Spike, Stem, and Total Biomass
2.5. Canopy Feature Extraction and Selection
2.6. Modeling and Evaluation
3. Results
3.1. Measured Biomass of Winter Wheat
3.2. Correlation Between Biomass Before and After Rectification and Indices
3.3. Evaluation of Leaf, Spike, Stem, and Total Biomass
3.4. Evaluation of Leaf Biomass Under Different Treatments
4. Discussion
4.1. The Rectified Biomass Improved the Representation of the Plot
4.2. Comparison of Leaf, Spike, Stem and Total Biomass Results
4.3. Comparison of Different Machine Learning Models
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Treatment | Supplemental Irrigation Schedule | Growth Stage Irrigated | Replicates |
---|---|---|---|
T1 | 3 April 2021 + 3 May 2021 | Jointing + flowering | 3 |
T2 | None | No irrigation | 3 |
T3 | 29 November 2020 | Wintering | 3 |
T4 | 10 March 2021 | Green-up | 3 |
T5 | 3 April 2021 | Jointing | 3 |
T6 | 10 April 2021 | 7 days after jointing | 3 |
T7 | 18 April 2021 | 14 days after jointing | 3 |
Vegetation Index | Name | Formula | References |
---|---|---|---|
NDVI | Normalized difference vegetation index | ( − )/( + ) | [37] |
GNDVI | Green normalized difference vegetation index | ( − )/( + ) | [38] |
SAVI | Soil adjusted vegetation index | 1.5 × ( − )/( + + 0.5) | [39] |
OSAVI | Optimized soil adjusted vegetation index | ( − )/( − + 0.16) | [40] |
Ratio vegetation index | [41] | ||
EVI2 | Two-band enhanced vegetation index | 2.5 × ( − )/( + 2.4 × + 1) | [42] |
WDRVI | Wide dynamic range vegetation index | (0.12 × − )/(0.12 × + ) | [43] |
DVI | Difference vegetation index | − | [41] |
GCI | Green chlorophyll index | − 1 | [44] |
RECI | Red-edge chlorophyll index | − 1 | [44] |
GRVI | Green–red vegetation index | ( − )/( + ) | [41] |
NDRE | Normalized difference red-edge | ( − )/( + ) | [45] |
NDREI | Normalized difference red-edge index | ( − )/( + ) | [46] |
MCARI | Modified chlorophyll absorption in reflectance index | (( − ) − 0.2 × ( − )) × () | [47] |
MCARI/OSAVI | MCARI/OSAVI | [47] | |
IPVI | Infrared percentage vegetation index | /( + ) | [48] |
MSR | Modified simple ratio | ( − 1)/( + 1) | [49] |
Acquisition Date | Sector | Number of Samples | Minimum | Maximum | Mean | Standard Deviation | Coefficient of Variation (%) |
---|---|---|---|---|---|---|---|
Heading Stage | Leaf | 231 | 1.955 | 5.417 | 3.404 | 0.714 | 20.97% |
Spike | 0.921 | 5.684 | 2.604 | 0.833 | 31.98% | ||
Stem | 3.613 | 14.79 | 8.611 | 2.097 | 24.35% | ||
Total | 7.976 | 24.662 | 14.619 | 3.268 | 22.35% | ||
Flowering Stage | Leaf | 231 | 1.824 | 6.699 | 3.726 | 0.936 | 25.13% |
Spike | 1.547 | 8.01 | 3.761 | 1.205 | 32.05% | ||
Stem | 5.115 | 21.209 | 10.491 | 2.773 | 26.43% | ||
Total | 9.161 | 34.59 | 18.007 | 4.522 | 25.11% | ||
Filling Stage | Leaf | 231 | 1.784 | 4.963 | 3.141 | 0.636 | 20.26% |
Spike | 4.675 | 17.74 | 10.224 | 2.692 | 26.33% | ||
Stem | 4.345 | 14.886 | 9.399 | 2.157 | 22.95% | ||
Total | 11.599 | 35.594 | 22.763 | 4.923 | 21.63% |
Stage | Sector | Indicators |
---|---|---|
Heading | Leaf | , GRVI, MCARI/OSAVI, MAE(), VAR(), CON(), COR(), HOM() |
Spike | NDVI, GRVI, MCARI/OSAVI, MAE(), VAR(), HOM(), COR(), Height | |
Stem | MAE(), VAR(), DIS(), SEM(), COR() | |
Total | / | |
Flowering | Leaf | GRVI, NDREI, RECI, MAE(), VAR(), DIS(), SEM(), COR(), CSI |
Spike | NDVI, MCARI/OSAVI, MAE(), VAR(), SEM(), COR(), Height, NRCT | |
Stem | DVI, GCI, RECI, COR(), CSI | |
Total | DVI, GCI, RECI, CSI | |
Filling | Leaf | DVI, GRVI, MCARI/OSAVI, VAR(), HOM(), ENT(), COR(), Height, CSI |
Spike | MCARI/OSAVI, RECI, CON(), ENT(), Height | |
Stem | DVI, MCARI/OSAVI, MAE(), SEM(), Height, CSI | |
Total | IPVI, MCARI/OSAVI, NDREI |
Flight Date | Models | Sector | Train Dataset | Test Dataset | ||||
---|---|---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE | MAE | |||
April 28 | RF | Leaf | 0.552 | 0.097 | 0.073 | 0.605 | 0.09 | 0.068 |
Spike | 0.493 | 0.106 | 0.08 | 0.493 | 0.106 | 0.081 | ||
Stem | 0.381 | 0.307 | 0.226 | 0.327 | 0.377 | 0.271 | ||
Total | / | / | / | / | / | / | ||
SVM | Leaf | 0.419 | 0.109 | 0.08 | 0.575 | 0.079 | 0.062 | |
Spike | 0.56 | 0.09 | 0.071 | 0.572 | 0.091 | 0.069 | ||
Stem | 0.116 | 0.37 | 0.277 | 0.114 | 0.4 | 0.302 | ||
Total | / | / | / | / | / | / | ||
NN | Leaf | 0.55 | 0.094 | 0.072 | 0.66 | 0.08 | 0.055 | |
Spike | 0.548 | 0.098 | 0.069 | 0.654 | 0.098 | 0.072 | ||
Stem | 0.303 | 0.333 | 0.269 | 0.276 | 0.339 | 0.278 | ||
Total | / | / | / | / | / | / | ||
May 12 | RF | Leaf | 0.653 | 0.106 | 0.076 | 0.709 | 0.114 | 0.091 |
Spike | 0.578 | 0.137 | 0.107 | 0.666 | 0.154 | 0.117 | ||
Stem | 0.615 | 0.336 | 0.258 | 0.616 | 0.302 | 0.242 | ||
Total | 0.442 | 0.604 | 0.463 | 0.445 | 0.676 | 0.516 | ||
SVM | Leaf | 0.526 | 0.129 | 0.105 | 0.557 | 0.117 | 0.099 | |
Spike | 0.371 | 0.167 | 0.128 | 0.458 | 0.154 | 0.125 | ||
Stem | 0.149 | 0.452 | 0.343 | 0.13 | 0.52 | 0.39 | ||
Total | 0.144 | 0.733 | 0.543 | 0.076 | 0.854 | 0.681 | ||
NN | Leaf | 0.659 | 0.102 | 0.073 | 0.65 | 0.116 | 0.072 | |
Spike | 0.557 | 0.14 | 0.102 | 0.572 | 0.139 | 0.109 | ||
Stem | 0.468 | 0.355 | 0.29 | 0.551 | 0.401 | 0.328 | ||
Total | 0.21 | 0.74 | 0.59 | 0.179 | 0.665 | 0.54 | ||
May 21 | RF | Leaf | 0.57 | 0.087 | 0.064 | 0.588 | 0.071 | 0.066 |
Spike | 0.454 | 0.343 | 0.266 | 0.449 | 0.373 | 0.296 | ||
Stem | 0.436 | 0.304 | 0.233 | 0.473 | 0.332 | 0.288 | ||
Total | 0.331 | 0.742 | 0.591 | 0.258 | 0.725 | 0.563 | ||
SVM | Leaf | 0.507 | 0.086 | 0.076 | 0.466 | 0.094 | 0.08 | |
Spike | 0.245 | 0.403 | 0.302 | 0.237 | 0.441 | 0.356 | ||
Stem | 0.378 | 0.306 | 0.246 | 0.419 | 0.352 | 0.282 | ||
Total | 0.094 | 0.849 | 0.698 | 0.102 | 0.856 | 0.707 | ||
NN | Leaf | 0.621 | 0.078 | 0.053 | 0.648 | 0.065 | 0.046 | |
Spike | 0.458 | 0.348 | 0.269 | 0.458 | 0.34 | 0.243 | ||
Stem | 0.454 | 0.3 | 0.249 | 0.432 | 0.301 | 0.249 | ||
Total | 0.193 | 0.787 | 0.653 | 0.213 | 0.873 | 0.77 |
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Liu, J.; Zhang, W.; Wu, Y.; Ma, J.; Zhang, Y.; Liu, B. Estimation of Leaf, Spike, Stem and Total Biomass of Winter Wheat Under Water-Deficit Conditions Using UAV Multimodal Data and Machine Learning. Remote Sens. 2025, 17, 2562. https://doi.org/10.3390/rs17152562
Liu J, Zhang W, Wu Y, Ma J, Zhang Y, Liu B. Estimation of Leaf, Spike, Stem and Total Biomass of Winter Wheat Under Water-Deficit Conditions Using UAV Multimodal Data and Machine Learning. Remote Sensing. 2025; 17(15):2562. https://doi.org/10.3390/rs17152562
Chicago/Turabian StyleLiu, Jinhang, Wenying Zhang, Yongfeng Wu, Juncheng Ma, Yulin Zhang, and Binhui Liu. 2025. "Estimation of Leaf, Spike, Stem and Total Biomass of Winter Wheat Under Water-Deficit Conditions Using UAV Multimodal Data and Machine Learning" Remote Sensing 17, no. 15: 2562. https://doi.org/10.3390/rs17152562
APA StyleLiu, J., Zhang, W., Wu, Y., Ma, J., Zhang, Y., & Liu, B. (2025). Estimation of Leaf, Spike, Stem and Total Biomass of Winter Wheat Under Water-Deficit Conditions Using UAV Multimodal Data and Machine Learning. Remote Sensing, 17(15), 2562. https://doi.org/10.3390/rs17152562