The Modern Forecasting Toolkit
The process fuses data from multiple sources, feeding it into intelligent models that learn to recognize patterns and predict future yields.
Satellite Imagery
NDVI & Remote Sensing Data
Climate Data
Temperature & Precipitation
Data Fusion & ML Models
XGBoost, Random Forest, etc.
Yield Prediction
Accurate Forecasts
Model Performance Showdown
Comparing the accuracy (R² score) of various machine learning models. A higher R² score indicates a better fit and more accurate prediction.
Key Predictive Factors
Analysis reveals which climate and satellite data points have the most influence on wheat yield predictions.
The Power of Data Fusion
Combining climate data with satellite imagery (NDVI) significantly boosts prediction accuracy, creating a more robust and reliable forecasting model.
Best Model Without Climate Data
81%
(XGBoost - R² Score)
Model With Climate-NDVI Fusion
89%
(Proposed Fusion Model - R² Score)