• M.G.Divyanjali
• P.Sruthi
• K.Lohitha
• M. Siva Kumar
• K. Prem Kumar

This work presents an Edge AI-powered food calorie estimation system for real-time dietary assessment using Raspberry Pi. The system employs a USB web camera integrated with a YOLO-based deep learning model to identify food items, while a load cell connected to Arduino measures the weight of the food. By combining food recognition and weight measurement, the system estimates calorie content and nutritional values accurately. The Raspberry Pi performs on-device inference, enabling offline operation without depending on cloud services. A buzzer provides alerts when calorie intake surpasses predefined thresholds, thereby supporting personalized health monitoring. Designed to be cost-effective, portable, and intelligent, this system is highly beneficial for health-conscious individuals, dieticians, and clinical nutrition management by providing real-time food analysis and dietary guidance at the edge.

Edge AI, Food Recognition, Calorie Estimation, Raspberry Pi, YOLO, Computer Vision, Load Cell, Embedded System, Real-Time Monitoring, Health Tracking

"Edge AI Powered Food Calorie Estimation for Real Time Dietary Assessment on Raspberry Pi", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 3, page no.g58-g63, March-2026, Available :http://www.jetir.org/papers/JETIR2603606.pdf

"Edge AI Powered Food Calorie Estimation for Real Time Dietary Assessment on Raspberry Pi", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 3, page no. ppg58-g63, March-2026, Available at : http://www.jetir.org/papers/JETIR2603606.pdf