MACHINE LEARNING BASED LIVESTOCK AND WILDLIFE ANIMAL DETECTION

Abstract

The use of science and technology to monitor wildlife enclosures in a specific region and to ensure animal security is known as wildlife monitoring using a raspberry pi. Animals escaping cages and hurting both people and other animals have been a frequent occurrence at zoo parks recently. people have also occasionally fallen into animal enclosures. Consequently, a system that can track these circumstances was devised. This technology is used for animal surveillance and security to identify intruders who enter the animal area as well as to determine whether any animals have fled or gone missing from their enclosure. Using machine learning, this system could identify the burglar who entered the container. The device is made up of raspberry pi camera and SD card circuitry interfaced to a raspberry pi B+ board, The raspberry pi camera takes the video of the cage and gives to the raspberry pi, then the obtained video streaming data is analyzed using open cv platform.

The system comprises of a Raspberry Pi B+ board with interfaces for the camera and SD card. The raspberry pi camera records video of the cage and transfers it to the raspberry pi. The video streaming data is then processed using the open cv platform for analysis. In the open cv platform, machine learning techniques are used to classify the data. To determine if an intruder entered the cage or if the animal fled, the data is evaluated. IoT is used to send notifications to the caregiver if any of the aforementioned scenarios materialize.