AI Model for Predicting Irrigation Needs (Smart Farming Solution)

This project presents a complete Machine Learning solution for predicting irrigation needs in agricultural fields using environmental and soil data.

The system classifies irrigation requirements into three levels: Low, Medium, and High, helping optimize water usage and support smart farming decisions.

Key Features:

Advanced Feature Engineering (ET Proxy, Water Balance, Soil Retention)

Multiple models used: Neural Networks, XGBoost, LightGBM

High accuracy and robust performance

Handles real-world agricultural data

Full preprocessing (scaling, encoding, imbalance handling)

Hyperparameter tuning for optimal results

Model ready for deployment (saved model, scaler, encoder)

Workflow:

Data Cleaning & Exploration

Feature Engineering

Data Preprocessing

Model Training & Comparison

Model Evaluation

Final Model Saving

This project demonstrates the use of AI in solving real-world agricultural challenges and improving resource efficiency