Abstract
In machine learning, feature extraction is a very important step in the construction of any pattern classification that extracts relevant features to identify the class from group of images. To recognizing object, accuracy depends upon the quality of features extracted from an image. Unique feature extraction has high accuracy in recognizing classes. In Deep learning image recognition is based on such as strong feature extraction ability and high recognition accuracy. In this paper we have discussed all the approaches used to recognize cattle from traditional to deep learning approaches, we have also analyzed the comparison of machine and deep learning approaches. We tried to explore few models of deep learning and its architecture that may result with high accuracy.
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