Smart Camera Traps in the Field: A Scoping Review of Architecture and Energy Evidence

Edge Artificial Intelligence (Edge AI) is used in wildlife monitoring to reduce communication demand, support decision-making, and enable energy-aware operation, yet embedded inference alone does not establish whether a system can sustain useful service after installation. This scoping review searched four bibliographic databases through 5 August 2026 and complemented these searches with studies identified from prior work on the topic, identifying 41 eligible English-language papers published from 2021 onward on camera-primary intelligent wildlife systems. The review aimed to assess operational maturity, field-validated operational role, energy evidence, and operational continuity. Full texts were charted and analyzed across sensing, decision-making, communication, response, energy, and continuity. Post-installation evidence exposed triggering, environmental, connectivity, maintenance, and energy-supply constraints not established through non-field evaluation. Field-validated alert delivery did not extend to quantitative validation linking human action to mitigation outcomes, while field activation of physical intervention alone did not establish behavioral or management effectiveness. Energy evidence was even more limited, with limited direct operational electrical measurement, rare main-state characterization, and no complete lifetime energy budget evidence. By separating architectural capability from the evidence supporting its operational realization, the review provides a systems-level basis for interpreting how far reported functions are actually validated. Across the corpus, optimization often shifts constraints among computation, triggering, communication, energy supply, and availability rather than eliminating them. Wildlife Edge AI should therefore be evaluated as a complete field system under application-relevant conditions.