Enhancing cattle production and management through convolutional neural networks. A review

Abstract

The rise in demand for animal products associated with global population growth has driven the world toward precision livestock farming, where convolutional neural networks (CNN) have gained increasing attention due to their potential to enhance animal health, productivity, and welfare. However, the effectiveness and generalizability of CNN applications in cattle production are limited by several challenges and limitations, which require further research and development to address. This systematic literature review aims to provide a comprehensive overview of the applications of CNN in cattle production. It identified some potential applications of CNN in this field and highlighted the challenges and limitations that need to be addressed to improve the effectiveness and efficiency of CNN applications in cattle production. It also provides valuable insights for researchers, practitioners, and policymakers interested in the use of CNN to enhance cattle production practices, animal welfare, and sustainability. Additionally, it also provides the reader with a summary of the literature on the fundamental concepts of convolutional neural networks and their commonly used model architectures in cattle production. This is because agriculture digitalisation is going more multidisciplinary and people from different areas of expertise may find it helpful to learn more from a combined source.