Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data and Pre-Processing
2.2.1. Statistical Data
2.2.2. Winter Wheat Vector Data and Phenological Periods
2.2.3. Remote Sensing Data
2.3. Machine Learning Methods for Yield Prediction
2.3.1. Linear Models and Regularization Methods
2.3.2. Decision Tree Models and Their Extensions
2.3.3. Distance-Based Models
2.3.4. Support Vector Regression Models
2.3.5. Ensemble Models
2.4. Winter Wheat Yield Prediction
2.5. Accuracy Assessment
2.6. Construction of Spatialization Model for Yield
3. Results
3.1. Comparison of Accuracy in Yield Prediction Algorithms for Entire Growth Period
3.2. Comparison of Accuracy in Yield Prediction Algorithms for Individual Growth Periods
3.3. Pixel-Level Spatialization of Yield
4. Discussion
4.1. Performance Comparison of Winter Wheat Yield Prediction Models
4.2. Analysis of Yield Prediction Potential for Individual Growth Periods
4.3. Analysis of Spatialization in Yield Research
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Phenology | Emergence | Tillering | Overwintering | Green-Up | Jointing | Heading | Milk Ripening | Maturation |
---|---|---|---|---|---|---|---|---|
Time | Late September to late October | Early November to early December | Mid-December to mid-February | Mid-February to mid-March | Mid-March to early April | Mid-April to late April | May | Early June to Late June |
Category | Variables | Spatial Resolution | Temporal Resolution | Sources |
---|---|---|---|---|
Statistical data | Yield | County-level | Yearly | Statistical Yearbook [39] |
Wheat area | ||||
Vector data | Wheat | County-level | Yearly | Sentinel 2 Image Extraction |
Vegetation index | EVI | 500 m | 8-day | MOD09A1 |
NDVI | MOD09A1 | |||
FPAR | MOD15A2H | |||
LST | 1 km | MOD11A2 | ||
Ecological data | GPP | 500 m | 8-day | MOD17A2H |
LAI | MOD15A2H | |||
Hydrological data | ET | 500 m | 8-day | MOD16A2 |
PET |
Models | Validation Data | Test Data | ||||
---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE | MAE | |
Elastic Net | 0.52 | 933.28 | 767.95 | 0.52 | 930.86 | 754.47 |
Gradient Boosting | 0.78 | 631.74 | 507.25 | 0.78 | 633.30 | 462.28 |
Random Forest | 0.69 | 756.39 | 611.46 | 0.75 | 676.49 | 517.72 |
Ridge | 0.86 | 509.50 | 389.66 | 0.79 | 609.80 | 475.07 |
Adaboost | 0.61 | 848.46 | 725.36 | 0.57 | 878.83 | 731.44 |
KNeighbors | 0.61 | 847.14 | 685.25 | 0.63 | 821.96 | 614.30 |
DecisionTree | 0.55 | 904.67 | 628.91 | 0.49 | 954.56 | 700.81 |
ExtraTree | 0.32 | 1116.44 | 812.83 | 0.38 | 1054.95 | 752.55 |
NuSVR | 0.25 | 1172.38 | 1036.04 | 0.26 | 1156.47 | 1021.89 |
SVR | 0.03 | 1332.69 | 1165.48 | 0.04 | 1316.57 | 1161.38 |
Ensemble Voting | 0.90 | 439.21 | 351.28 | 0.90 | 424.44 | 313.92 |
Removed Model | Validation Data | Test Data | ||||
---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE | MAE | |
Gradient Boosting | 0.82 | 569.00 | 458.29 | 0.86 | 506.82 | 386.23 |
Random Forest | 0.88 | 477.62 | 381.62 | 0.88 | 474.53 | 349.19 |
Ridge | 0.77 | 652.50 | 526.04 | 0.78 | 636.83 | 470.86 |
None (Initial Model) | 0.90 | 439.21 | 351.28 | 0.90 | 424.44 | 313.92 |
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Lou, Z.; Lu, X.; Li, S. Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning. Agronomy 2024, 14, 1834. https://doi.org/10.3390/agronomy14081834
Lou Z, Lu X, Li S. Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning. Agronomy. 2024; 14(8):1834. https://doi.org/10.3390/agronomy14081834
Chicago/Turabian StyleLou, Zhengfang, Xiaoping Lu, and Siyi Li. 2024. "Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning" Agronomy 14, no. 8: 1834. https://doi.org/10.3390/agronomy14081834