Smart Feeder Aquarium and Water Quality Monitoring using Raspberry PI and Blynk

Authors

  • Putera Mohamad Nasri Ahmad Saiful Bahri Universiti Tun Hussein Onn Malaysia Author
  • Danial Md Nor Universiti Tun Hussein Onn Malaysia Author

Keywords:

smart pet feeder, water quality monitoring, Raspberry Pi 4 Model B, Turbidity Sensor, pH Sensor, Servo Motor

Abstract

The Smart Pet Feeder system is an innovative solution designed to automate pet care, particularly for aquatic environments, by integrating sophisticated sensor technology and artificial intelligence. The system features a pH sensor and a turbidity sensor, both critical for maintaining optimal water conditions for fish. The pH sensor continuously monitors the water's acidity or alkalinity, ensuring that it remains within a healthy range specific to the species of fish being cared for. This is crucial because even slight deviations in pH levels can lead to stress, illness, or even death in fish. The turbidity sensor, on the other hand, measures the water's clarity by detecting suspended particles such as uneaten food, waste, or algae. High turbidity levels can reduce water quality by lowering oxygen levels and increasing the risk of disease. To further enhance the system's functionality, the Smart Pet Feeder incorporates the YOLOv5 model, a cutting-edge deep learning algorithm known for its speed and accuracy in real-time object detection. YOLOv5 is utilized in the system to monitor the movement of pets, specifically fish, within the aquarium. The model processes the video feed from a built-in camera and detects when the fish are active, ensuring that the feeding mechanism is triggered only when the fish are in motion and near the feeding area. This approach not only reduces food waste but also prevents overfeeding, which can lead to poor water quality and health issues in fish. The entire system is built around a Raspberry Pi 4 Model B microcontroller, which serves as the central hub for processing data from the sensors and controlling the feeding mechanism. During evaluation, the system demonstrated high effectiveness in automating the feeding process and maintaining optimal water conditions, with the sensors providing accurate readings and the YOLOv5 model delivering precise motion detection. In conclusion, the Smart Pet Feeder system represents a comprehensive and advanced approach to pet care. By combining precise sensor technology with intelligent AI-driven motion detection, the system ensures that pets, particularly fish, are fed appropriately and live in a well-maintained environment. This not only simplifies the responsibilities of pet owners but also significantly enhances the health and well-being of their pets, providing peace of mind even when they are away from home.

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Published

06-11-2024

Issue

Section

Computer and Network

How to Cite

Putera Mohamad Nasri Ahmad Saiful Bahri, & Danial Md Nor. (2024). Smart Feeder Aquarium and Water Quality Monitoring using Raspberry PI and Blynk. Evolution in Electrical and Electronic Engineering, 5(2), 190-198. https://penerbit.uthm.edu.my/periodicals/index.php/eeee/article/view/17522