  Insects are the most important global pollinator of crops and play a key role
in maintaining the sustainability of natural ecosystems. Insect pollination
monitoring and management are therefore essential for improving crop production
and food security. Computer vision facilitated pollinator monitoring can
intensify data collection over what is feasible using manual approaches. The
new data it generates may provide a detailed understanding of insect
distributions and facilitate fine-grained analysis sufficient to predict their
pollination efficacy and underpin precision pollination. Current computer
vision facilitated insect tracking in complex outdoor environments is
restricted in spatial coverage and often constrained to a single insect
species. This limits its relevance to agriculture. Therefore, in this article
we introduce a novel system to facilitate markerless data capture for insect
counting, insect motion tracking, behaviour analysis and pollination prediction
across large agricultural areas. Our system is comprised of edge computing
multi-point video recording, offline automated multispecies insect counting,
tracking and behavioural analysis. We implement and test our system on a
commercial berry farm to demonstrate its capabilities. Our system successfully
tracked four insect varieties, at nine monitoring stations within polytunnels,
obtaining an F-score above 0.8 for each variety. The system enabled calculation
of key metrics to assess the relative pollination impact of each insect
variety. With this technological advancement, detailed, ongoing data collection
for precision pollination becomes achievable. This is important to inform
growers and apiarists managing crop pollination, as it allows data-driven
decisions to be made to improve food production and food security.
