๐Ÿ“Š Enter Crop Parameters

Fill in the agricultural data to predict crop yield

๐ŸŒค๏ธ Weather Conditions โœ“ ML Active

Annual rainfall in mm (0-3000)
Average temperature (0-50ยฐC)
Relative humidity (0-100%)
Daily sunshine hours (4-10)
GDD accumulation (0-5000)
Atmospheric pressure (99-103)
Average wind speed (5-30)

๐ŸŒฑ Soil Properties โœ“ ML Active

pH value (4.5-9.0)
Quality index (40-100)
Organic carbon % (0.3-2.5)
Moisture content (20-80%)
Nitrogen content (10-300)
Phosphorus content (10-170)
Potassium content (10-450)

๐Ÿšœ Farm Management โœ“ ML Active

Fertilizer amount (0-500 kg/ha)
Type of irrigation system
Type of seed variety
Type of soil in field
Market price per unit (100-50000)

๐ŸŒพ Crop & Location โœ“ ML Active

Type of crop being cultivated
State where crop is grown
District name (optional)
Growing season
India's 15 agro-climatic zones

๐Ÿ“ Farm Area & Location ๐Ÿ“‹ Contextual

Your farm/field size
Field centroid latitude
Field centroid longitude
Altitude above sea level
Planting/sowing date
Expected harvest date

Compare predicted yields across different crop types with same conditions

๐Ÿ“ˆ Prediction Results

๐ŸŒพ

Enter crop parameters and click "Predict Yield" to see results

โ„น๏ธ Model Information

Trained on 75,000 synthetic crop records

๐Ÿค–

Model Type

Gradient Boosting

๐Ÿ“Š

Rยฒ Score

0.9627

๐Ÿ“‰

MAE

1,610 kg/ha

๐Ÿ“

RMSE

3,574 kg/ha

๐ŸŒพ

Crops

22 types

๐Ÿ—บ๏ธ

States

20 Indian states

๐Ÿ“ˆ Model Analysis

Prediction accuracy and data quality visualizations

๐ŸŽฏ Prediction Analysis

Prediction Analysis

Actual vs Predicted yields showing Rยฒ = 0.9627

๐Ÿ” Data Quality Analysis

Outlier Analysis

Outlier detection on original dataset before synthetic generation