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 intru-sion detection, edge artificial intelligence (AI) and location tracking for farm protection, giving priority to IEEE journal publica-tions 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, loca-tion 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 archi-tecture 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 experimen-tally validated.