Animal Behavior and Well-Being Symposium I: Precision Technology and Animal Welfare
Recent advances in bio-telemetry technology have made possible to generate lot of data through sensors which could be used to monitor welfare and classify behavioural activities in many different farm animals. However, little has been done w.r.t evaluating predictive ability and comparing various machine learning approaches for ‘big data’ and also evaluating how this change depending on sampling frequencies and position of sensors.
In this talk I will discuss technological development covering range of sensor technologies utilising state of art computation and transmission protocols we have co- developed as part of our research and on how we used these technologies to build machine learning algorithms for lameness in sheep and cows, drinking behaviour in cows with an ultimate aim to improve animal welfare. Algorithms could classify behaviours with overall accuracy above 95%, however, the accuracy varied by number of features used, choice of algorithm and window size used for feature generation. The talk will focus on challenges and approaches to build smart systems that are not only technologically advanced, have good accuracy, algorithms that continue to learn and versatile but also energy efficient and practical. While precision livestock farming has been a growing area for the past decade and has huge potential to improve livestock health and welfare, technology adoption has not occurred at the same pace. We need to understand farmers perception and understanding around technology, its use on farm and in farming. Results from our research with farmers suggest few key areas are important for embedding and adoption of technology on farms: first, utility of the technology, lack of validation and its ability to fit with existing structures and practices and the beliefs held by farmers that the use of the device may result in a loss of skill in future -that of the farmer knowing his animals.