Design and Development of a Smart Pet Feeder with IoT and Deep Learning †

Abstract

The well-being of pets is essential for owners. This project developed an automatic pet feeder that leverages Internet of Things technology and deep learning to address feeding challenges. The feeder integrates sensors, including a weight sensor for portion control, a camera for pet identification, an ultrasonic sensor for proximity detection, and a servo motor for dispensing food. A microcontroller for real-time monitoring and processing controls these components. Based on YOLOv5 and trained on a dataset of dog images, the DL model ensures accurate pet recognition and customized feeding. Results show that the system effectively identifies pets and dispenses appropriate portions based on weight, ensuring precise and personalized feeding. The sensor data fusion provides reliable information about pet characteristics. Overall, the smart feeder offers a convenient and efficient solution for managing pet nutrition, improving pet health, and increasing owner convenience.