Development of an AI-Integrated Smart Weighing Bowl with Temperature Detection

Abstract

Recent advancements in artificial intelligence (AI), sensor technologies, and Internet of Things (IoT) systems have transformed traditional kitchen environments into more intelligent and efficient food preparation spaces. However, many existing kitchen devices remain limited to single-purpose functionalities, requiring users to operate multiple tools separately for weighing, monitoring temperature, and recipe management. This study presents the development of an AI-Integrated Smart Weighing Bowl with Temperature Detection designed to improve cooking accuracy, workflow efficiency, and intelligent user assistance during food preparation. The proposed system integrates Arduino-based hardware components, including a load cell sensor with an HX711 amplifier module and a DS18B20 temperature sensor, together with a Flutter-based mobile application and Firebase Firestore cloud database. An AI-powered recommendation module was incorporated to analyze user inputs and measurement data in order to provide contextual recipe suggestions, ingredient guidance, and cooking alerts in real time. The system architecture was developed using an Agile-Kanban methodology to support iterative planning, software development, hardware integration, testing, and evaluation. Functional testing demonstrated that the prototype was capable of monitoring ingredient weight and temperature while simultaneously providing AI-assisted cooking recommendations through a mobile dashboard interface. Firebase integration enabled reliable cloud synchronization for user records, cooking history, and personalized preferences. Although full real-time sensor-to-application synchronization remains under further optimization, the developed prototype successfully established the feasibility of combining intelligent sensing, cloud communication, and AI-driven analysis within a single smart kitchen platform. The study contributes to the growing field of smart kitchen technologies by offering a scalable and user-centered framework that enhances food preparation accuracy, reduces manual workload, and promotes more informed cooking decisions for both household and professional culinary environments.