Can AI be used to track pollinators in strawberry crops?

Deep learning algorithms, referred to as AI, are being applied to digital video to identify four different pollinator species working in a strawberry farm and to plot the insects’ visits to flowers.

A study conducted during his PhD and being continued by Dr. Malika Ratnayake in his capacity as a postdoctoral researcher at Monash University forecasts a system that automatically reports which pollinators visit a crop, how often they visit, and from what direction – potentially superseding the expensive and time-consuming manual methods of pollinator assessment.

This is part of a broader project being led by Associate Professor Alan Dorin in the Faculty of Information Technology, Monash University, to explore how technology can be used to improve insect pollination security of crops and native ecosystems in the face of changing climate.

More than a third of the world’s food production from crops relies on animal pollination, Ratnayake says. Each pollination-dependent crop has different requirements around the optimal number of visits a flower needs from effective insect pollinators to maximize fruit yield and quality.

But at a point in human history when there has never been a greater need for efficient pollination of crops, climate change is affecting the pollinators’ ability to do their job – partly through direct effects on the individual insects and partly because shifting climate zones are bringing new insect species into conflict with pollinator species.


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