AI & ML BASED PET FEEDING SYSTEM USING IMAGE PROCESSING

Abstract

The core of the system relies on image processing techniques and ML-based classification models such as Convolutional Neural Networks (CNNs) to accurately recognize pets. Once a pet is detected and verified, a microcontroller (e.g., Raspberry Pi or Arduino) triggers a feeding mechanism to release the food. This entire process is contactless and fully automated, promoting a hygienic and efficient feeding environment. Moreover, the system stores feeding data and images, which can be used for monitoring pet behavior and ensuring compliance with dietary schedules. To further enhance user interaction and accessibility, the system can be linked with a mobile or web application. Through this interface, users can monitor feeding logs, receive notifications, and even customize feeding schedules remotely. The use of cloud storage enables data analytics, allowing pet owners to understand feeding trends and potentially detect health issues early. Additionally, integration with IoT components like weight sensors or smart collars could offer advanced tracking capabilities.