Abstract
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.
Highlights
Confirming the presence of livestock activity is necessary to be eligible for a series of subsidies
YOLOv1 runs at 45 FPS, much faster than other object detection algorithms, such as Faster RCNN [20], but its main drawback is that it is not as accurate
Recognition of livestock activity is necessary in order to apply for certain subsidies
Summary
Confirming the presence of livestock activity is necessary to be eligible for a series of subsidies. In order to qualify for the subsidy, it is necessary to declare which land the agricultural or livestock activity is being carried out on. It is crucial to create a service to automate this task since, currently, it is very common for an operator to have to go on site to check whether a piece of land complies with the declaration made. Drone images are used, which are manually reviewed by an operator This manual process is very costly due the large surface area to be checked: the European Union has 4 million km. This work will help to identify the presence of livestock activity, reducing the cost and increasing the speed with which the declaration is confirmed
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