Smart Farm Fence Monitoring and Intrusion Detection System with AI-Based Location Track-ing: A Review and Conceptual Architecture

Crop damage and theft caused by wild animals, stray animals and human intruders remain a persistent problem for farmers, and a passive fence neither detects nor reports a breach. This paper presents a focused narrative review of verified studies on intrusion detection, edge artificial intelligence (AI) and location tracking for farm protection, giving priority to IEEE journal publications and supplementing them with IEEE conference and open-access sources. Four themes are examined: camera-based AI detection and deterrence; low-cost sensor-triggered and LoRa-based fog or edge systems; GNSS and LoRa-based position tracking of animals; and validation practice. The reviewed work shows that lightweight deep-learning detectors can run on low-power edge devices, that a PIR-and-camera strategy has been reported to save energy, and that signal-strength information can reduce the energy spent on continuous GPS. However, the accessible evidence is dominated by prototype-scale studies, location tracking is rarely integrated with intrusion detection, and false-alarm rate, detection latency and independent validation are seldom reported. The paper contributes a comparative table of representative studies, a proposed seven-layer conceptual architecture for a smart farm fence that combines IR/PIR sensing, camera-based AI, GPS-collar tracking and geofencing, a leakage-resistant evaluation protocol, and a prioritised list of research gaps. The architecture is conceptual and has not been experimentally validated.