Optimizing Real-Time Product Recognition and Payment Automation in IoT-Enabled Smart Shopping Carts

The research focuses on the development of a smart shopping cart system equipped with real-time item recognition and automated billing functionalities, aimed at enhancing the efficiency and convenience of the retail shopping experience. The proposed system utilizes an ESP32-CAM module for product identification, enabling seamless item recognition without manual input. Items placed in the cart are automatically recognized through image processing, with corresponding product details displayed on an LCD screen for user convenience. A central processing unit (CPU) manages data flow, ensuring accurate real-time updates and cost calculations. The system also integrates a payment gateway, allowing for contactless transactions, which significantly reduces checkout times. To further enhance usability, a buzzer is employed to provide auditory feedback during item addition or removal, and the system is powered by a 9V battery for portability. Cloud services are incorporated for data storage and inventory management, ensuring that the system remains scalable and capable of handling large data volumes. Power consumption and accuracy of item recognition have been key considerations throughout the design, with testing showing reliable performance under various conditions. The system offers a practical solution to common inefficiencies in the shopping process, aiming to improve both the customer experience and operational flow in retail environments. Future improvements in energy efficiency and algorithm optimization are anticipated to increase the system’s commercial viability and functionality.