Farmland Intrusion Detection using Internet of Things and Computer Vision Techniques
Автор: Iyinoluwa M. Oyelade, Olutayo K. Boyinbode, Olumide S. Adewale, Emmanuel O. Ibam
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 2 Vol. 16, 2024 года.
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Farmland security in Nigeria is still a major challenge and existing methods such as building brick fences around the farmland, installing electric fences, setting up deterrent plants with spikey branches or those that have displeasing scents are no longer suitable for farmland security. This paper presents an IoT based farmland intrusion detection model using sensors and computer vision techniques. Passive Infrared (PIR) sensors and camera sensors are mounted in strategic positions on the farm. The PIR sensor senses motion by the radiation of body heat and sends a message to the raspberry pi to trigger the camera to take a picture of the scene. An improved Faster Region Based Convolutional Neural Network is developed and used for object detection and One-shot learning algorithm for face recognition in the case of a person. At the end of the detection and recognition stage, details of intrusion are sent to the farm owner through text message and email notification. The raspberry pi also turns on the wade off system to divert an intruding animal away. The model achieved an improved accuracy of 92.5% compared to previous methods and effectively controlled illegal entry into a farmland.
Internet of Things, Computer Vision, Convolutional Neural Networks, Sensors, Intrusion Detection
Короткий адрес: https://sciup.org/15019384
IDR: 15019384 | DOI: 10.5815/ijitcs.2024.02.03
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