Native Swift application with on-device AI that identifies individual cattle via computer vision, operating fully offline in remote ranch environments.
The Problem
Ranchers need to manage hundreds of head of cattle across thousands of acres with no reliable cellular coverage. Traditional livestock management requires manual record-keeping, leading to errors, lost data, and inability to track individual animal health and lineage. Existing mobile solutions required constant internet connectivity and failed in rural environments.
The Solution
Built a native Swift application leveraging iOS Core ML and Vision frameworks for on-device computer vision. The app can identify individual cattle from photos using facial recognition techniques adapted for livestock, storing all data locally in SQLite. Implemented a sophisticated store-and-forward sync mechanism that queues operations and intelligently syncs when connectivity is detected. Ranchers can photograph cattle, record health data, track lineage, and manage inventory entirely offline.
The Impact
Enabled ranchers to maintain digital records in environments where traditional software fails. On-device processing means zero latency and no dependency on connectivity. The store-and-forward architecture ensures no data loss even after days offline. Ranchers reported 80% time savings in record-keeping and significantly improved accuracy in tracking individual animal health and genetics. The computer vision system achieved 95%+ accuracy in individual cattle identification.