Livestock Monitoring System

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend.

What I Built

Livestock Monitoring System (LMS) is a technology-based solution designed to help farmers monitor livestock and improve farm management.

The system combines Raspberry Pi hardware, environmental sensors, a camera, ultrasonic sensors, and motor controls to support remote monitoring and automated operations.

Its key capabilities include live camera monitoring, obstacle detection, motorized controls, environmental data collection, and alerts when obstacles are detected. The goal is to make livestock monitoring more convenient and help farmers keep track of their surroundings and farm operations.

The project is intended to address the practical needs of farmers who want to monitor livestock remotely and reduce the need for constant manual observation.

Demo

Live Demo: Not currently listed in the repository.

Demo Video: Not currently listed in the repository.

Code

GitHub Repository: RISHABH12005/LMS

Explore the source code, hardware components, software libraries, existing features, and planned enhancements in the repository.

How I Built It

The LMS project combines hardware and software components to support livestock monitoring and automation.

Hardware

• Raspberry Pi 4B: Main controller for the system.
• Raspberry Pi Sense HAT: Collects environmental measurements, including temperature, humidity, and pressure.
• Raspberry Pi Camera: Supports visual monitoring.
• Ultrasonic sensors: Detect obstacles in the system's surroundings.
• LEGO Mindstorms motors and BrickPi: Support motorized movement and automation.

Software and libraries

• Python: Implements hardware control and obstacle detection.
• FastAPI and Uvicorn: Provide the backend and application server.
• WebSockets: Support real-time communication.
• OpenCV: Supports image processing and computer vision.
• Picamera2 and Sense HAT libraries: Interface with the camera and environmental sensors.
• Ngrok: Supports remote access during development.

The repository also lists PyTorch and Ultralytics among its software tools.

AI component: The README describes AI-driven livestock health analysis and Raspberry Pi AI HAT+ support as future enhancements. An open-source AI model could be integrated to interpret available sensor readings and explain monitoring alerts. However, this AI extension must be implemented and tested before it can be presented as a completed feature.

Why Does Open Innovation Matter?

Open innovation makes livestock-monitoring technology easier to inspect, adapt, and improve.

By building on open-source hardware and software, developers can customize the system for different farm environments, add compatible sensors, and improve its features without depending entirely on a single commercial platform.

Open-weight AI models could also provide more control over deployment and farm-data handling, depending on the model, hardware, and configuration.

This approach creates opportunities for developers, farmers, and open-source communities to collaborate on practical, accessible tools for livestock management.

My Agent Session

No DevRelay agent-session link is currently available.

Prize Categories

No partner prize category has been confirmed for this repository. Add a category only after implementing and documenting the relevant partner technology, or remove this section if it does not apply.