Santhoshkumar
Ramesh

Problem: Manual crop disease identification is slow, error-prone, and inaccessible to farmers in remote regions.

Approach: Engineered a Transformer-based classification model using PyTorch and OpenCV, trained on 40+ disease classes with data augmentation pipelines.

Outcome: Achieved 91.2% validation accuracy. Integrated an intelligent treatment recommendation engine. Deployed as a containerized REST API via FastAPI + Docker for reproducible, scalable inference.

Problem: Emergency vehicles lose critical minutes in traffic, directly impacting patient survival rates.

Approach: Built an AI-powered traffic management system leveraging IoT sensors, GPS tracking, LoRa communication, and drones to provide clear, optimized routing.

Outcome: Won multiple hackathons. Achieved measurably faster ambulance transit times by dynamically rerouting traffic signals in real time.

Problem: Network infrastructure failures cause costly downtime, and manual monitoring cannot scale.

Approach: Engineered an IoT-driven solution collecting real-time network performance metrics via embedded sensors. Built statistical anomaly detection logic to identify irregularities before they escalate.

Outcome: Proactively flags network anomalies with automated alerts, significantly reducing mean time to detection and downtime risk.