We’re thrilled to announce that the GrazeSense precision livestock monitoring system has successfully completed comprehensive testing, demonstrating exceptional performance that exceeds all project targets. After months of rigorous evaluation across multiple scenarios, the results confirm that GrazeSense is ready to revolutionize pasture management for farmers across Europe.
Outstanding Test Results
Our testing program evaluated the complete system across 1,991 individual measurements spanning six AI detection sessions and multiple network performance scenarios. The results speak for themselves:
AI Detection Excellence: The YOLOv11-based computer vision system achieved an impressive 72% detection accuracy—exceeding our 60% target by a significant margin. Operating on edge computing hardware aboard autonomous drones, the system processes video in real-time with inference times of just 20-25 milliseconds per frame, enabling farmers to monitor their livestock as events unfold.
Satellite Communication Reliability: Integration with advanced satellite communication infrastructure demonstrated remarkable robustness. The system maintained 96.2% uptime across all testing conditions, with zero packet loss even under challenging signal conditions. Network throughput of 6-9 Mbps provides comfortable margins above the 2.5 Mbps encoding requirement, supporting future expansion to multiple simultaneous drone operations.
Real-World Readiness: Perhaps most impressively, the system maintained perfect stability throughout testing—zero frame drops across nearly 2,000 measurements. This reliability ensures farmers can trust GrazeSense for critical pasture management decisions without worrying about missing data or system failures.
Validated Under Diverse Conditions
The testing program didn’t just evaluate ideal scenarios. We deliberately stressed the system under challenging conditions including:
- Cloudy sky simulations with atmospheric interference and partial signal degradation
- Severe signal attenuation equivalent to heavy weather conditions (50dB variation)
- Variable livestock densities from empty fields to clustered flocks of 35+ sheep per frame
- Extended operations validating sustained performance over complete mission durations
Across all scenarios, GrazeSense demonstrated the robustness and adaptability that real-world agricultural operations demand.
What This Means for Farmers
These technical achievements translate directly into practical benefits for livestock farmers:
- Accurate livestock counting eliminates manual counting errors that typically range ±30-50%
- Pasture condition monitoring through NDVI analysis helps optimize grazing rotation
- Remote operation capability via satellite enables monitoring of distant or hard-to-reach pastures
- Multi-camera scalability supports expansion from pilot deployments to farm-wide coverage
- Weather resilience ensures reliable operation even during overcast or rainy conditions
The system’s combination of precision, reliability, and ease of use positions GrazeSense as a transformative tool for sustainable livestock management.
Field Demonstrations Confirm Usability
Beyond laboratory testing, GrazeSense completed two successful field demonstrations with actual farmers. User acceptance testing confirmed the intuitive nature of the web and mobile interface, with operators successfully planning and executing autonomous missions with minimal training. Farmer feedback has been overwhelmingly positive, validating that the technology delivers genuine value for day-to-day farm operations.
Looking Ahead
With comprehensive testing complete and system performance validated, GrazeSense moves forward toward broader deployment and commercialization. The robust technical foundation established through this testing phase provides confidence that the platform can scale to serve farms of all sizes across diverse European agricultural regions.
We’re particularly excited about the headroom for future enhancements revealed during testing. The 2.4-3.6× throughput margin suggests the system can readily support higher resolution cameras, additional NDVI sensors, or multiple simultaneous drone swarms without infrastructure upgrades.
Project Achievements at a Glance
- 72% AI detection accuracy (target: >60%)
- 96.2% satellite link uptime (target: >95%)
- 20-25ms real-time inference (budget: 40ms for 25 FPS)
- Zero frame drops across 1,991 measurements
- Zero packet loss across all network tests
- 2.4-3.6× bandwidth margin for future expansion
- Successful field demonstrations with farmer validation
