Cattle Breed Detection and Categorization Using Image Processing and Machine Learning

Researchers have had numerous opportunities to construct new analytical procedures in various scientific domains due to the development of machine learning and soft computing techniques. Livestock identification procedures are used to categorize different breeds of cattle based on their size and colour. A distinctive radio frequency, image frequency, or ear tag is necessary for the current methods used to predict specific breeds of cattle. We advise using different cattle picture datasets for the identification of the breed of cattle. The front, back, and sides of cattle will be photographed to extract key features, and these feature extractions will be carried out to uniquely identify each cattle with its information. The breed of cattle will be identified based on the processed images from each set of image data. Following picture preprocessing, each cow's images will be divided up by breed. Based on a preprocessed image dataset of cattle breeds, another category of rare cattle breeds is identified. In this study, the images of cattle facial morphology and various body shapes are recognized using the SVM machine learning algorithm. We may learn about Breed's fundamental characteristics, such as colour, average milk production, average body mass, etc., thanks to the proposed approach.