The management of livestock is rapidly evolving with the onset of new technological developments. A paper published in Livestock Science by Nasirahmadi and colleagues reviewed the use of machine vision for observing the behaviour of cattle and pigs, and revolutionising how the health and well-being of animals can be monitored.
Through the use of machine vision, the challenges associated with the more traditional “hands-on” approaches to monitoring livestock are virtually eliminated. According to the authors of this paper, machine vision techniques allow for automatic, non-contact, low stress, and cost-effective means for managing livestock.
For instance, the live weight of individual livestock can be automatically calculated based on data gained from 2D and 3D imaging. Similarly, body shape, physical condition and behavioural characteristics can also be assessed from such imaging, indicating the animal’s health. This includes identifying movement, lying, feeding and drinking, aggression, and reproductive behaviours.
However, to enhance this technology’s applicability, the authors also recommended tracking livestock using drone mounted sensors. That is, radio-tagging individual animals could provide “greater reliability than image analysis due to various uncontrollable conditions in indoor and outdoor farm environments, in combination with the fact that the animals in a group (i.e. cattle and pigs) can be highly similar in shape, colour and size.”
Given our team at Wildlife Drones has already developed a system that can track radio-tagged animals using drones, it can be readily applied within agricultural settings as well without the need for any on-ground infrastructure. Therefore we are keen to further explore opportunities for helping farmers to locate and manage their stock in a more efficient, safe and non-invasive ways. If you know any livestock managers who may be interested in exploring this opportunity with us, we’d love to hear from you.
To learn more about this technology and how it may benefit productive stock management, we recommend reading the original paper: Nasirahmadi, A., Edwards, S.A., & Sturm, B. (2017). Implementation of machine vision for detecting behaviour of cattle and pigs. Livestock Science, 202, 25-38.