IoT / Animal Health / AgriTech

Pigeon Pulse

Intelligent Avian Health Monitoring System

Smart health-monitoring ecosystem for pigeons using lightweight IoT devices to track vitals, detect early symptoms of illness, and enable real-time care decisions in large aviaries.

Timeline

6-8 months

Team Size

4-6 engineers

Pigeon Pulse Project

Technologies Used

IoT SensorsMQTTPythonNode.jsAWS IoT CoreDynamoDBReact NativeFlutterGrafanaBLE Modules

The Challenge

Pigeon caretakers in large aviaries faced a critical problem: detecting illness early enough to save birds. Traditional methods relied on manual observation—inconsistent, time-consuming, and often too late.

There was no digital system in place. Everything was logged manually, observations were spotty, and by the time symptoms became visible, disease had often spread throughout the flock.

Our Mission

We set out to automate health tracking, identify anomalies early, and give caretakers real-time visibility into the health status of every bird.

Reduce mortality rates through early detection
Improve early diagnosis capabilities
Create a data-driven care model

What We Delivered

Wearable IoT sensors for pigeons
Gateway-based data collection system
Cloud backend for analytics (AWS IoT Core)
Mobile and web dashboards (React Native)
Temperature & heart-rate detection
Motion analysis algorithms
Real-time alert system
Historical trend analysis
Caretaker assignment workflows

Pain Points & Challenges

Tiny Sensors for Tiny Birds

Designing ultra-lightweight, non-invasive IoT sensors that pigeons could wear comfortably was a major engineering constraint.

Erratic Connectivity

Aviaries presented challenging RF environments with signal drop-offs and interference from building structures.

Battery Life Issues

Continuous monitoring drained batteries quickly, requiring innovative power management solutions.

No Historical Datasets

Lack of existing health data for training anomaly detection models made baseline creation difficult.

How We Overcame Them

Aggressive Power Management

Designed custom firmware with advanced sleep modes and wake-on-motion protocols to extend battery life by 300%.

Edge Filtering

Implemented intelligent edge computing to filter and compress data at the source, minimizing packets sent to the cloud.

RF Optimization

Strategically placed RF repeaters and optimized antenna placement to eliminate connectivity dead zones.

Crowdsourced Baseline Model

Created a pseudo-baseline health model using aggregated data from multiple aviaries to establish normal ranges.

Results & Impact

40-60% improvement in disease detection time

70% reduction in manual monitoring workload

Higher survival rates through early intervention

Predictable care cycles and resource allocation

Real-time visibility for caretakers across multiple locations

"The client reported higher survival rates and more predictable care cycles, transforming how they manage aviary health."

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