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RoadmapStart narrow. Prove it. Then expand.
A realistic plan that begins with post-stun verification for one farmed species in one controlled context — buildable even with recorded video and simulated process data — and grows through validation toward a deployable platform.
One species. One context. Real evidence.
Video-based post-stun risk flagging, with manual expert review and basic environmental/process logging — for a single farmed species (e.g. rainbow trout, sea bass, or sea bream) in one controlled setting. Deliberately narrow, so it is achievable and verifiable within months.
What we build at 1, 3, and 6 months.
- Month 1–2
Map & define
State-of-the-art review, expert/advisor interviews, workflow mapping, target species, and a fail-safe welfare-indicator framework. Output: requirements + first species profile + pilot protocol.
- Month 2–4
Build Lite + Sensor Box prototype
Offline app + dashboard, low-cost temperature/conductivity logging, batch audit report, and a simulated/recorded-data demo. Output: working MVP device + bill of materials.
- Month 4–6
Semi-field pilot
Test with a realistic workflow, compare logs to manual observation, measure usability and operator burden, refine alert thresholds, and document cost per site. Output: pilot report.
- Month 6–9+
Optional camera assist & validation
Collect post-stun video, label visible failure/recovery signs, train a conservative edge model, and design the expert-validation protocol. Output: camera-assist prototype + validation plan.
How the work is organized.
WP1 — State of the art & requirements
Review standards & literature, pick target species, define indicators and what the system can/cannot claim, advisor review. → SOTA report + requirements.
WP2 — Welfare indicator framework
Specify the Welfare Risk Index across visual, process, environmental, operational signals + expert review. → risk-index specification.
WP3 — Data acquisition prototype
Camera + sensor inputs, timestamp synchronization, batch/event logging, local storage, basic dashboard. → working MVP device.
WP4 — AI risk detection
Detection, tracking, motion/anomaly analysis, multimodal classifier, uncertainty score → human review. → risk-detection prototype.
WP5 — Dashboard & reporting
Live view, alerts, batch history, annotated clips, corrective-action log, exportable reports, advisor review panel. → web application.
WP6 — Pilot validation & business model
Validate anomaly detection, missed-event reduction, usability, audit value, and affordability. → pilot report + business case.
