Design and Implementation of Intelligent Pet Feeding System

Keeping pets can help people regulate their emotions, engage in physical activity, and cultivate friendships, all of which contribute to enhancing their overall quality of life. Based on a survey, pets are often left alone at home for an average of 8 hours, as their owners typically depart early and return late. Nonetheless, the current pet feeding systems available on the market exhibit issues like a restricted field of view, unreliable network connections, sluggish pet recognition speed, and subpar accuracy. In this paper, we introduce a more intelligent and efficient pet feeding system. This system leverages the fast and compact ResNet18 model and utilizes Jetson Nano and STM32F407ZGT6 chips to achieve pet image acquisition and species recognition functionalities. We employ the MQTT protocol to enable the uploading of environmental data and have designed a user-side webpage for convenient remote monitoring and timely checking of the pet's status. Furthermore, we implement an end-to-end interaction design, allowing users to remotely and flexibly adjust factors such as the amount of pet feeding, environmental temperature, humidity, and other related information. Lastly, we conducted real-world deployment and testing of the system in various households, achieving a remarkable pet recognition accuracy of 98.65%. This system effectively fulfills the requirements for a scientific, automated, and efficient approach to pet care in daily life.