Mobile App for Muzzle Pattern Recognition for Cattle Identification Using Feature Extraction

Cattle identification is a crucial aspect of livestock management, influencing various processes such as tracking, insurance, and health monitoring. Traditional methods often rely on manual tagging, which can be cumbersome and prone to errors. The aim of the study is to develop a mobile application utilizing muzzle pattern recognition for cattle identification. By harnessing state-of-theart algorithms including Convolutional Neural Networks (CNN), ResNet50, and VGG-16, the application captures and processes muzzle images of cattle during registration. Through intricate feature extraction techniques, unique feature matrices are derived and stored in a centralized database. During subsequent identification processes, the application swiftly retrieves and compares feature matrices from the database, facilitating rapid and accurate cattle identification. With a userfriendly interface and versatile applications in livestock management, insurance, and beyond, the solution aims to revolutionize cattle identification in agriculture.