Transforming weight measurement: a cutting-edge IoT-enabled smart weight machine for centralized price control of products

Background: With the rapid advancement of machine learning technology, there is a growing interest in integrating it into IoT systems for enhanced functionality. In this study, we propose a SMART Weight Machine system designed to detect, weigh, and price various objects using machine learning techniques. Methods: Our system utilizes TensorFlow, a machine learning framework, in conjunction with Raspberry Pi for object recognition. Image processing is performed locally on the Raspberry Pi for efficient detection. The system also incorporates MySQL for database management and a WebApp developed using PHP and Laravel for the user interface.   Results: Through our implementation, we achieved significant improvements in speed and accuracy. TensorFlow's compatibility with microcontroller devices like Raspberry Pi enabled swift processing, resulting in a 96% accuracy rate for object detection during our evaluation. Conclusions: The SMART Weight Machine system demonstrates promising potential for real-world applications. Moving forward, rigorous testing and quality assurance will be conducted to ensure the reliability and accuracy of the system during the development phase.