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.

Insecurity is one of the major challenges that the entire world is facing now, each country having their peculiar security issues. The crime rate in every part of the society these days has become a threatening issue such that vehicles are now used for committing criminal activities more than before. The issue of vehicle theft has increased tremendously, mostly at gunpoint or car parks. In view of these, there is a need for adequate records of stolen, identified and recovered vehicles which are not readily available in our society and as such very important. The development of a vehicle theft alert and location identification system becomes more necessary for vehicle owners to ensure theft prevention and a speedy identification towards recovery efforts in situations where a vehicle is missing, stolen or driven by an unauthorized person. The theft alert function makes use of a GSM application developed and installed in a mobile phone device which is embedded in the vehicle to communicate with the vehicle owner's mobile phone. The communication is established via SMS (i.e. between the installed mobile phone device and that of the vehicle owner). The communications established include; (i). Sending an SMS alert from installed mobile phone device to vehicle owner mobile phone when the car ignition is put on. (ii). Sending an SMS from the vehicle owner's mobile phone to start and stop the installed mobile phone device application. The location identification function makes use of a web application developed to; (i). Determine the real time location of a vehicle by means of tracking using GPS. (ii). Broadcast missing or stolen vehicle information to social media and security agency. The implementation of the installed mobile phone device application was done using JAVA because of its capabilities in programming mobile applications while PHP and MySQL was used for the web application functions. Integration testing of the system was carried out using simple percentage calculation for the performance evaluation. Fifty seven (57) vehicle owners were sampled and questionnaires were distributed to them in order to ascertain the acceptability and workability of the developed system. The result obtained shows the effectiveness of the system and hence it can be used to effectively monitor vehicle as it is been driven within or outside its jurisdiction. More so, the system can be used as database of missing, identified or recovered vehicles by various security agencies.