The recognition of livestock activity is essential to be eligible for subsides, to automatically supervise critical activities and to locate stray animals. In recent decades, research has been carried out into animal detection, but this paper also analyzes the detection of other key elements that can be used to verify the presence of livestock activity in a given terrain: manure piles, feeders, silage balls, silage storage areas, and slurry pits. In recent years, the trend is to apply Convolutional Neuronal Networks (CNN) as they offer significantly better results than those obtained by traditional techniques. To implement a livestock activity detection service, the following object detection algorithms have been evaluated: YOLOv2, YOLOv4, YOLOv5, SSD, and Azure Custom Vision. Since YOLOv5 offers the best results, producing a mean average precision (mAP) of 0.94, this detector is selected for the creation of a livestock activity recognition service. In order to deploy the service in the best infrastructure, the performance/cost ratio of various Azure cloud infrastructures are analyzed and compared with a local solution. The result is an efficient and accurate service that can help to identify the presence of livestock activity in a specified terrain.
Clustering Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO
