Novel semio-chemical approach to control the Australian Sheep Blowfly . The Australian Federal Government through the 'Smart Farming' initiative highlights the need for improved multidisciplinary measures in order to remain at the global forefront of the invention and adoption of technology. This multidisciplinary project (entomology, biotechnology, analytical chemistry and genomics) will rapidly inform the management of fly strike on an important Australian resource merino sheep. This will bui ....Novel semio-chemical approach to control the Australian Sheep Blowfly . The Australian Federal Government through the 'Smart Farming' initiative highlights the need for improved multidisciplinary measures in order to remain at the global forefront of the invention and adoption of technology. This multidisciplinary project (entomology, biotechnology, analytical chemistry and genomics) will rapidly inform the management of fly strike on an important Australian resource merino sheep. This will build the key biochemical data in order to develop a novel fly lure technology (at scale) to be used on farm delivering national benefit through improved animal welfare and safety considerations for producers, and will establish the best approach to disseminate this scientific information to stakeholders such as farmers.Read moreRead less
Artificial intelligence algorithms to predict risk of injury in racehorses. This project will address the urgent need for predicting and preventing catastrophic and career limiting limb injuries and cardiac arrhythmias in racehorses due to over (or under) training. Using data from GPS and movement sensors integrated into saddlecloths, artificial intelligence algorithms will convert cumulative data on speed, gait, and stride characteristics during training, along with injury data, into a risk mat ....Artificial intelligence algorithms to predict risk of injury in racehorses. This project will address the urgent need for predicting and preventing catastrophic and career limiting limb injuries and cardiac arrhythmias in racehorses due to over (or under) training. Using data from GPS and movement sensors integrated into saddlecloths, artificial intelligence algorithms will convert cumulative data on speed, gait, and stride characteristics during training, along with injury data, into a risk matrix. Recorded heart rate and ECG data will also be analysed using artificial intelligence to detect early evidence of the development of cardiac arrhythmias. The system will improve racehorse welfare, providing a simple interface to warn trainers when risk of injury becomes high, in order to prevent catastrophic breakdown.Read moreRead less