Crop Intelligence Platform

Predict the most suitable crop and fertilizer plan from field conditions

Combining machine learning with agronomic expertise to deliver data-driven crop recommendations based on soil nutrients, climate patterns, and regional characteristics.

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ML Model

Random Forest

Handles mixed features and nonlinear agronomic relationships

Training Data

1920 records

16 crop classes with balanced sampling

Model Accuracy

96.1%

Validated with cross-validation on test set

Generate Crop Recommendation

Enter your field conditions to receive AI-powered crop and fertilizer recommendations

1 Soil Nutrients

Core fertility indicators that influence crop suitability.

Nitrogen (kg/ha)

0–200

Phosphorus (kg/ha)

0–120

Potassium (kg/ha)

0–180

Soil pH (pH)

4.0–9.0

Organic Matter (%)

0–8

Soil Moisture (%)

0–100

2 Weather Profile

Climate inputs that shape growth potential and yield stability.

Temperature (°C)

0–45

Humidity (%)

0–100

Rainfall (mm/year)

0–3500

Sunlight (hours/day)

0–14

Altitude (meters)

0–3500

3 Location and Management

Geographic and field settings used for contextual recommendations.

Soil Type

Agro-climatic Region

State

Season

Irrigation Method

How It Works

Our AI-powered platform combines machine learning with agricultural expertise

Data Collection

Input soil nutrients (N, P, K), climate data (temperature, humidity, rainfall), and regional characteristics for comprehensive analysis.

ML Processing

Random Forest algorithm analyzes patterns across 16 features to identify optimal crop matches with confidence scoring.

Smart Recommendations

Receive crop predictions with fertilizer plans, alternative options, and condition-specific insights for informed decision-making.

High Accuracy

96.1% validation accuracy

16 Crop Types

Comprehensive crop database

Real-time Analysis

Instant predictions

Fertilizer Plans

Customized recommendations