A non-Restraining Sheep Activity Detection and Surveillance using Deep Machine Learning

The number of livestock farms and their sizes (particularly the sheep farms) are on the rise, in response to the growing demands of food supply chain for increasing population. The detection and monitoring of sheep activities particularly in huge farms is tedious and challenging task. Therefore, a reliable and cost-effective sheep activity detection system which can be utilized for the virtual fencing, is of high demand. Existing data-driven approaches use accelerometer data for sheep monitoring and activity detection however, there are several limitations with these methods such as generating high volume of data with noise, relatively expensive, and not very reliable. This study presents a non-invasive computer vision-based approach along with deep transfer learning for sheep detection and determining whether the corresponding state is ‘active’ or ‘inactive’. We complied a primary dataset comprising sheep in diverse poses and activities in a realistic outdoor environment. A custom YOLOV5s model is trained over new dataset and validated on purely unseen sheep instances for the model evaluation. The statistical outcomes demonstrate the robustness of proposed approach for various sheep activity detection. Our method has diverse implications and uses in the development of reliable and economical systems for monitoring sheep, particularly in extensive farms and virtual fencing applications.