Design and development of a smart pet feeder with IoT and Deep learning

Introduction

Proper nutrition for pets is crucial for their well-being. This project addresses feeding issues by developing an automatic pet feeder with Internet of Things (IoT) technology and deep-learning (DL) techniques. The device is designed to dispense appropriate food portions, enhancing pet nutrition management and overall health.

Methods

The automatic pet feeder integrates multiple sensors and deep learning algorithms, specifically convolutional neural networks. The sensor network includes a weight sensor for precise measurement, a camera for pet identification, an ultrasonic sensor for detecting proximity, and a servomotor for controlled food dispensing. Data from these sensors are processed using a microcontroller with Wi-Fi capabilities, facilitating real-time monitoring. The DL model was trained using a dataset of images of dogs and cats to ensure accurate identification and customized feeding plans.

Results

Testing has shown that the DL system can identify pets with precision and accurately dispense appropriate food portions based on weight, enabling species-specific feeding and providing real-time monitoring. The integration and fusion of sensors provided reliable data on food consumption and pet weight, optimizing feeding quantities. Alerts and notifications were successfully transmitted to pet owners via an application, ensuring continuous monitoring and adjustment of feeding patterns and reassuring them about their pets' safety and well-being.

Conclusions

The automatic pet feeder achieved its objectives of providing a convenient, reliable, and adaptable solution for managing pet nutrition. Its ability to customize feeding based on individual pet needs, combined with IoT and DL technologies, highlights its potential for improving pet health and owner convenience. Future enhancements will focus on refining sensor accuracy, expanding functionalities, and further incorporating more advanced DL techniques to personalize pet care and feeding routines. This project demonstrates the potential of IoT-based and DL solutions in promoting pet well-being through precise and automated nutritional management.