Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Processing
2.2.1. Rice Yield
2.2.2. NDVI
2.2.3. Auxiliary Variables
2.3. Stack-Ensemble Model
2.4. Accuracy Assessment Metrics
3. Result
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Models | Average RMSE | Average MAE |
---|---|---|
NDVI vs. Yield | ||
LR | 685.17 | 636.83 |
RF | 556.19 | 507.45 |
Gradient Boost | 575.03 | 562.04 |
LightGBM | 545.85 | 502.08 |
XGBoost | 552.27 | 507.02 |
Stack Ensemble | 451.05 | 425.35 |
NDVI + Auxiliary Variables vs. Yield | ||
LR | 550.94 | 514.50 |
RF | 361.52 | 337.19 |
Gradient Boost | 372.03 | 359.04 |
LightGBM | 356.85 | 334.55 |
XGBoost | 355.90 | 333.12 |
Stack Ensemble | 328.06 | 317.21 |
District | Yield 2018 | Predicted Yield 2018 | Percentage of Error 2018 (%) | Yield 2019 | Predicted Yield 2019 | Percentage of Error 2019 (%) | RMSE | MAE |
---|---|---|---|---|---|---|---|---|
JHAPA | 4260 | 4144 | 2.7 | 4403 | 4203 | 4.5 | 163.62 | 158.08 |
MORANG | 4140 | 3931 | 5.05 | 4237 | 3953 | 6.7 | 248.96 | 246.08 |
SUNSARI | 3980 | 3574 | 10.20 | 3987 | 3656 | 8.3 | 370.41 | 368.55 |
BARA | 4060 | 3773 | 7.0 | 4137 | 3928 | 5.1 | 251.22 | 248.20 |
DHANUSHA | 3980 | 3654 | 8.2 | 3983 | 3501 | 12.1 | 411.30 | 403.83 |
MAHOTTARI | 3460 | 3179 | 8.1 | 3474 | 2937 | 15.4 | 428.44 | 408.79 |
PARSA | 4110 | 3994 | 2.8 | 4134 | 3997 | 3.3 | 126.61 | 126.17 |
RAUTAHAT | 3410 | 3191 | 6.4 | 3443 | 3024 | 12.17 | 334.25 | 318.90 |
SAPTARI | 3520 | 3073 | 12.7 | 3564 | 3119 | 12.5 | 445.77 | 445.77 |
SARLAHI | 3520 | 3068 | 12.8 | 3564 | 3104 | 12.9 | 455.45 | 455.43 |
SIRAHA | 3430 | 3078 | 10.2 | 3515 | 2744 | 21.9 | 599.29 | 561.04 |
CHITAWAN | 3970 | 3676 | 7.4 | 4002 | 3755 | 6.17 | 270.75 | 269.74 |
BANKE | 3150 | 2959 | 6.1 | 3476 | 3134 | 9.83 | 276.56 | 266.07 |
BARDIYA | 4310 | 4204 | 2.5 | 4300 | 3989 | 7.23 | 232.49 | 208.37 |
DANG | 4180 | 4120 | 1.4 | 4188 | 4281 | 2.2 | 78.40 | 76.45 |
KAPILBASTU | 3540 | 3517 | 0.6 | 3511 | 3188 | 9.2 | 228.34 | 172.50 |
RUPANDEHI | 4150 | 4474 | 7.8 | 4145 | 4542 | 9.5 | 362.59 | 360.79 |
KAILALI | 4160 | 3803 | 8.5 | 4270 | 3671 | 14.0 | 492.81 | 477.64 |
KANCHANPUR | 3880 | 3388 | 12.68 | 3893 | 3476 | 10.71 | 456.02 | 454.50 |
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Islam, M.D.; Di, L.; Qamer, F.M.; Shrestha, S.; Guo, L.; Lin, L.; Mayer, T.J.; Phalke, A.R. Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning. Remote Sens. 2023, 15, 2374. https://doi.org/10.3390/rs15092374
Islam MD, Di L, Qamer FM, Shrestha S, Guo L, Lin L, Mayer TJ, Phalke AR. Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning. Remote Sensing. 2023; 15(9):2374. https://doi.org/10.3390/rs15092374
Chicago/Turabian StyleIslam, Md Didarul, Liping Di, Faisal Mueen Qamer, Sravan Shrestha, Liying Guo, Li Lin, Timothy J. Mayer, and Aparna R. Phalke. 2023. "Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning" Remote Sensing 15, no. 9: 2374. https://doi.org/10.3390/rs15092374