Facial recognition of nelore cattle through computer vision
Keywords:
Machine Learning, Artificial Intelligence, Nelore, Precision Livestock Farming, Facial RecognitionAbstract
This article aims to develop a model for recognizing Nelore cattle based on images of their faces. The experiment employs a methodology involving the creation of a database of images containing 2,210 images of 47 Nelore cattle, along with identification annotations for each animal and facial segmentation information using the YoloV8 network. For recognition, the article utilizes feature extraction through embeddings with the Inception network. Finally, an analysis of the following machine learning algorithms is conducted: K Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (TREE), with precision values of 0.952, 0.987, and 0.635, respectively. This research is significant in the field of animal science as it proposes an innovative approach to Nelore cattle recognition using computer vision and machine learning technologies, contributing to more efficient and accurate monitoring and management of these animals.
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