saksham-1020/AGRI-SENSE-X

# FARM-SENSE X v2.5 ## Hybrid Intelligence Agriculture Advisory System # Overview FARM-SENSE X is an AI-powered smart agriculture advisory system developed to help farmers in crop disease detection, pesticide recommendation, and field-condition analysis. The system combines: * CNN-based crop disease detection * Rule-based agricultural expert system * XGBoost calibrated machine learning * Hybrid confidence fusion (CCI) The system provides intelligent recommendations using: * Crop images * Soil condition * Water level * Weather condition * Insect activity * Crop stage # Key Features * Hybrid AI + Rule-Based Intelligence * CNN Ensemble Disease Detection * Weather API Integration * Smart Spray Recommendation * Confidence Fusion System (CCI) * Digital Advisory Report Generation * Historical Trend Analysis * SQLite Database Logging * Explainable AI Output # Technologies Used * Python * TensorFlow * CNN (Convolutional Neural Network) * XGBoost * OpenCV * NumPy * Pandas * SQLite * Joblib # Project Structure AGRI-SENSE-X/ │ ├── src/ ├── models/ ├── data/ ├── reports/ ├── test_images/ ├── requirements.txt ├── README.md # System Workflow Farmer Input ↓ Image Analysis using CNN ↓ Rule Engine Evaluation ↓ XGBoost Confidence Calibration ↓ Hybrid Fusion (CCI) ↓ Final Recommendation ↓ Database Logging # How to Run ## Step 1: Install Dependencies pip install -r requirements.txt ## Step 2: Run the Project python src/run_agri_sense.py # Sample Features * Disease Detection from Leaf Images * Smart Spray Recommendation * Weather-Based Advisory * Confidence-Based Decision System * Digital Farmer Report Generation # Hybrid Intelligence Architecture The project combines: 1. CNN-based image intelligence 2. Rule-based agricultural reasoning 3. Calibrated ML confidence scoring This hybrid approach improves: * Reliability * Explainability * Decision accuracy * Practical field usability # Team Members * Saksham Tomar – AI Integration & Hybrid Intelligence * Shareen Khan – Data Handling & Database Management * Sharyu Dhamne – Rule Engine & Testing # Academic Purpose This project was developed as a Mini Project for B.Tech Computer Science Engineering. # Future Scope * IoT Sensor Integration * Mobile Application Support * Voice-Based Farmer Assistant * Offline AI Prediction * Expanded Crop & Disease Support * Drone-Based Field Analysis # License This project is developed for academic and research purposes.