Automatic Clothes Drying System with IoT and Gemini AI

Abstract

Unpredictable weather often disrupts household activities, particularly in drying clothes. Sudden rain may cause clothes to get wet again, requiring constant user supervision. This research aims to design and implement an automatic clothesline system based on the Internet of Things (IoT), integrating a rain sensor, webcam, and Gemini AI with the zero-shot image classification method. The system relies on two main inputs: a rain sensor to detect raindrops, and a webcam capturing sky images classified by Gemini AI into sunny, light cloudy, heavy cloudy, and rainy. These inputs are processed using a rule-based algorithm on the ESP32 microcontroller, which controls the servo motor to retract or extend the clothesline automatically. In addition to the automatic mode, the system is equipped with a manual control mode accessible through the Blynk mobile application, allowing users to monitor and control the system remotely. Testing results show that the rain sensor accurately detects water presence, while Gemini AI can classify weather conditions responsively. Integration testing confirms that the clothesline operates effectively in both automatic and manual modes, while providing real-time weather condition notifications.