ProTag

ProTag is a smart ear tag for livestock that can detect the early onset of illness in real-time; lowering costs and increasing animal welfare along with farmers peace of mind. Temperature, movement and location sensors are embedded into an ear tag. Data collected is run through algorithms on board that can determine animal states such as: Chewing, lying, Sleeping etc. This semi-processed data is then transmitted over LoRaWAN where it eventually lands in a cloud database and is combined with data from other animals as well as geographical farm features. All this data on animal metrics and behaviour can then be feed into continually improving machine learning models targeted at identifying early illnesses. If early cases could be identified, then many proactive actions could be taken such as: Mastitis: - Cows with clinical or subclinical cases can be milked last – preventing spread of the disease. - Back flushing could be performed on clusters of clinical or subclinical cases. - Tailored treatment protocols can be administered. Lameness: - Trim claw of cows identified as lame. - Isolate cases that are suspected to be infectious. - Implement foot bathing when needed. BVD: - Early identification of PI calves. - Ensuring early pregnant cows avoid contact with BVD infected herd mates. - Herd testing could be done early. Reproduction: - IVF - Optimized grazing for stage of pregnancy. - Separation of calf’s - Identification of complication during calving. There are several aspects that make our approach novel. The first is the incorporation of GPS with other metric data into a small formfactor ear tag. Products incorporating the same hardware features are usually attached an animal’s neck which can cause problems when caught on objects. The second is a wholistic integrated approach to data analysis. Information from an animal is not viewed in isolation, but against the backdrop of geographic farm features such as shelter belts, water troughs and boundary fences.

Baden Parr is currently pursuing a PhD degree with the Department of Mechanical and Electrical Engineering at Massey University, Auckland under the supervision of Dr Mathew Legg. His doctoral research focuses on automating grape yield estimation using 3D camera technologies. Furthermore, Baden is experienced in the development of embedded electronics, artificial intelligence (AI), acoustic signal processing, IoT sensor design, advanced motor control, and the development of innovative legged robots. Tyrel Glass is also a Massey University PhD student. He is under the supervision of Associate Professor Fakhrul Alam, and his research looks at using the communications ability of LED lighting for indoor positioning. Tyrel has experience in electronic design, machine learning, and IoT development. He is a lecturer for data science and machine learning courses. NATHANIEL FAULKNER received the B.E. degree (Hons.) in electronics and computer engineering from Massey University, New Zealand, in 2016, where he is currently pursuing a Ph.D. Nathaniel has experience with embedded software development, machine learning, the internet of things and full stack server development.