IOT-Based Farmland Intrusion Detection System - Ibam, Emmanuel; Boyinbode, Olutayo K.; Aladesiun, Helen (Information System; Universitas Buana Perjuangan Karawang, 2023)
As crop vandalization with conflicts between farmers and herdsmen become recurrent in Nigeria, existing farm intrusion prevention methods such as fence mounting and placement of farm guards can no longer guarantee farm security. This is because intruders either jump over the fence or attack guards on duty without visual evidence. Therefore, a complementary approach using computer technologies for effective detection is required. This paper presents an IoT-based farm intrusion detection model using RFID and image recognition technology. RFID sensor as well as cameras are placed at entrances of a fenced farmland for simultaneous identification. The sensor reads workers’ tags for identification, while cameras capture images of users for further identification as captured images are sent to Convolutional Neutral Network (CNN) for recognition. A user whose image cannot be recognized is flagged as an intruder and an intrusion alert with visual evidence is sent to the farm owner. The system showed a high level of effectiveness with an accuracy of 90%, Precision of 70%, and 80% Recall rate and effectively controlled the rate of illegal encroachment into farmland.