A new monitoring system developed by Monash University researchers is using AI to track bee movement, helping improve pollination and crop yield. The research, published in the International Journal of Computer Vision, worked to build a database of over 2000 insect tracks at a commercial strawberry farm in Victoria.
The research was supported by the Australian Research Council (ARC) Discovery Projects grant, the Monash-Bosch AgTech Launchpad Primer Grant, AgriFutures and the ARC Research Hub, and required a customised software to analyse the huge volume of data and reliably track individual insects flying through complex foliage.
The researchers hope to use the monitoring system in the future to study long-term impacts and results of precision pollination techniques, and how it changes the quality of food production and yield over several crop cycles.
Research co-author and NativeBee+Tech Facility lab director Associate Professor Alan Dorin, said traditional methods of insect monitoring on farms are time-consuming, labour intensive and can produce inaccurate or unreliable data.
“The monitoring system developed through this study can generate same-day data of crop pollination levels and provide farmers the evidence they need to inform decision-making,” Dorin said.
“Knowing the extent to which a crop has been pollinated allows growers to alter hive locations and numbers to boost pollination levels.
“Farmers might also open or close greenhouse sidewalls to encourage or discourage insect visits from particular directions. They may decide to add flowers to entice insects to explore crop regions that have not been pollinated adequately.
Dorin says simple interventions like these can ensure a better rate of successful pollination, and a higher yield of market-quality fruit.
“We believe that this system will serve as a benchmark for future research in precision pollination,” said Dorin.
The study’s lead researcher Dr Malika Ratnayake said a key challenge during the research was to identify the movement of individual insects within a video so that the same insect path was not accidentally counted multiple times.
“The advanced software developed for the system combines AI-based object-detection capabilities with separate foreground detection algorithms to identify the precise positions of insects and the flowers they visit in the recorded videos.
“We have opted to keep this software open-source so it is accessible to anyone who wants to build similar monitoring systems or other applications to optimise and analyse different data points captured through videos,” Ratnayake said.
The recordings from the Victorian strawberry farm were 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.
Building on this study, the researchers will be collaborating and working with the Australian Blueberry Growers Association, Costa Group’s berries division, CSIRO, Western Sydney University and University of New England.
The team is also exploring links to European insects via collaborations with the University of Trento, Italy and the German Centre for Integrative Biodiversity Research.
