Machine Learning

Soil Fertility Classification Using Machine Learning

The Soil Fertility Classification Using Machine Learning system is a comprehensive machine learning solution that classifies soil fertility status as Healthy, Moderate Stress, or High Stress using 12 key soil parameters. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced regression algorithms like Ridge and Lasso Regression to achieve 89.67% accuracy.

The system leverages features including soil moisture, temperature, pH, nitrogen, phosphorus, potassium, humidity, light intensity, and rainfall. It provides interactive visualizations, feature importance analysis, model comparison, and real-time soil fertility predictions to help farmers and agricultural researchers make data-driven decisions.

• Multi-Class Soil Fertility Classification
• Ridge Regression (89.67% Accuracy)
• Lasso Regression (87.23% Accuracy)
• 12 Soil Parameter Analysis
• Feature Importance Analysis
• Real-time Fertility Prediction
• Model Performance Comparison
• Interactive Visualizations
• 5-Fold Cross-Validation
• Flask Web Application