Farming with AI: Optimising pollination for better food production

A new monitoring system developed by Monash researchers uses artificial intelligence (AI) to track bees' movement to help improve pollination and crop yield. The research, published in the International Journal of Computer Vision , involved recording pollinators like honey bees, hover flies, moths, butterflies and wasps, to build a database of over 2000 insect tracks at a commercial strawberry farm in Victoria. The recordings were then analysed using Computer Vision and AI to track individual movements of individual insects, to count them, and to monitor their flower visits. This enabled farmers and researchers to understand the contributions of different species to pollination. Optimal pollination requires the right number of pollinator visits to flowers. Too few or too many visits, or visits by ineffective insect pollinators, can reduce the quality of food a flowering plant produces - ultimately impacting the yield. Research co-author, NativeBee+Tech Facility Lab Director Associate Professor Alan Dorin, from the Faculty of Information Technology, said traditional methods of insect monitoring on farms are time-consuming, labour intensive and can produce inaccurate or unreliable data.