Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete. This paper will analyse the benefits and drawbacks of each approach. The aim of this paper is to promote a discussion on whether knowledge of classical computer vision techniques should be maintained. The paper will also explore how the two sides of computer vision can be combined. Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning. For example, combining traditional computer vision techniques with Deep Learning has been popular in emerging domains such as Panoramic Vision and 3D vision for which Deep Learning models have not yet been fully optimised.

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The scale of dairy farming worldwide has changed significantly over recent years, with a move towards larger, more intensive, profit-driven enterprises, primarily due to market pressures. This change has resulted in demand for technologies that can reduce costs and labour inputs while increasing farm productivity. This is mainly achieved through the use of farm automation and advanced technological techniques. An important aspect of farm automation that is currently being researched is the area of automated animal health monitoring. In this research, we have identified specific diseases which are common in dairy animals which can be identified through the use of non-invasive, low-cost, sensor technology. These diseases have been mapped to specific aspects of animal behaviour that have been mapped to the three sensors which are most significant to identify these diseases. The identified sensors will be shown to be vital in the development of the next generation of health monitoring system for dairy animals. Such a system will allow the automatic identification of animal health events, greatly increasing overall herd health and yield while reducing animal health inspection and long-term animal healthcare costs.