The Challenge
End-to-end IoT + computer vision ecosystem that automated poultry operations and detected avian disease early.
The Approach
Adopted a scalable architecture as the central hub for data pipelines, enabling the creation of impactful workflows to drive growth and automation.
System Architecture Redesign
To achieve high availability, the infrastructure was entirely re-architected. We shifted from a monolithic bottleneck to a decentralized pipeline. The deployment required careful orchestration of databases and frontend technologies to ensure smooth delivery.
"By optimizing the platform availability, we fundamentally changed how the operational team makes decisions on a daily basis."
Project Execution
The deployment was critical. It wasn't just about moving data from point A to point B; it was about transforming raw logic into actionable insights on the web dashboard within milliseconds.
- Automated 100% of climate and feeding schedules in poultry operations by developing an IoT ecosystem using ESP32, Arduino, Raspberry Pi, and custom PCB Design.
- Mitigated flock mortality risks by designing an early avian disease detection system powered by a computer vision model analyzing stool image data.
- Reduced response time to agricultural health emergencies by building a vaccine recommendation module and deploying a real-time viral outbreak alert framework.
- Streamlined workforce management and livestock tracking by building a full-stack cross-platform software ecosystem.
Overcoming Bottlenecks
During the rollout phase, integration issues became a frequent occurrence due to high concurrent usage. The team resolved this by implementing an event-driven architecture utilizing robust deployment practices.
Key Outcomes
100%
Climate & feeding automation
CV-powered
Avian disease detection
Real-time
Outbreak alert framework
