The AI Shelf Stock Monitoring is an AI + IoT project that combines AI demand prediction and Low stock alerts, built with Raspberry Pi. The project follows a clean, modular pipeline where sensor data acquisition, AI processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical AI and IoT techniques while producing a working, demo-ready system.
Manual monitoring and decision making for this task is slow, inefficient, and cannot scale across many devices. Without an AI + IoT approach built on Low stock alerts and Raspberry Pi, users cannot collect data from the physical world and act on it intelligently in real time.
This project applies AI + IoT techniques through AI demand prediction, orchestrated with Raspberry Pi and Low stock alerts. The system is designed for reliability and intelligence, with sensing, connectivity, AI analysis, and clear evaluation. It produces consistent, reusable results and can be adapted to related smart applications with minimal changes.
Raspberry Pi
MQTT
ESP32 / Arduino / Raspberry Pi
MQTT / HTTP
Cloud dashboard
Machine learning models
ESP32
RFID reader
Weight sensor
Modular IoT pipeline around AI demand prediction and Low stock alerts
Configurable sensor and AI settings
Real-time data collection and monitoring
Clear alerts, logging, and error handling
Reusable components for related smart systems
AI based decision and prediction
The project is layered: the sensing layer acquires data through AI demand prediction; the connectivity and AI layer processes it with Raspberry Pi and Low stock alerts; and the output layer monitors and presents results via Shelf monitoring. Shared connectivity, alerting, and dashboard modules support all layers, keeping the system robust and easy to extend.
Set up the hardware, sensors, and development environment.
Build the sensor data acquisition layer with AI demand prediction.
Implement connectivity and AI processing using Raspberry Pi and Low stock alerts.
Add the dashboard and alert layer via Shelf monitoring.
Wire up end-to-end flows and add error handling and retries.
Test with real data, tune AI models, and refine the system.
Package the project, document it, and prepare the demo and viva report.
Build production-style AI + IoT applications
Apply Demand forecasting and Store dashboards
Connect and control IoT hardware
Analyze sensor data with machine learning
Present and defend a complete AI + IoT project in viva
Add edge AI for on-device decisions
Add more sensors and cloud services
Add mobile app control
Deploy on scalable cloud infrastructure
The AI Shelf Stock Monitoring delivers a complete AI + IoT workflow — from sensor data acquisition and connectivity to AI analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand AI and IoT skills.
