Projects

This project is an advanced agricultural AI system—the Smart Crop Stress Advisor—that predicts plant health status from IoT sensor data and provides actionable field recommendations. Unlike standard classification models, this system utilizes a Hybrid Machine Learning pipeline (combining Random Forest and SVM) to generate a 0-100 risk triage score. It processes 11 distinct sensor features, including soil moisture, chlorophyll levels, and nitrogen content, to provide a comprehensive view of crop health.

A critical technical hurdle addressed was the 'Black Box' nature of AI in agriculture. By integrating SHAP (Explainable AI), the system identifies the specific sensor drivers behind a stress prediction, allowing farmers to understand *why* a plant is at risk. The solution is deployed as a dockerized Streamlit application capable of both single-plant diagnosis and high-volume batch triage from CSV data, bridging the gap between machine learning research and practical field operations.