Every hive inspection produces scattered notes — stores, brood pattern, temper, mite counts. Turned into a small set of tracked KPIs and read across the season, those notes become decisions: when to feed, when to treat for varroa, when to add supers. This scene renders each hive's four core metrics as a live 3D bar chart, and rolls them up into a floating apiary-wide dashboard above the yard.
Beekeepers who log even three or four numbers per hive per visit — stores, brood frames, mite drop, temper — can spot a failing queen or a mite spike weeks before it would show up as dead colonies, simply by watching the trend line bend.
A live 3D apiary where every hive grows its own bar chart of core metrics, feeding a floating holographic dashboard that turns scattered inspection notes into a small set of tracked KPIs.
Four hive-level metrics — honey stores, brood pattern, population and mite load — evolve across a simulated season and roll up into an apiary-wide dashboard, mirroring how real inspection data becomes a decision tool.
Scrub through the season, set a mite alert threshold, pick which metric drives the dashboard and trend line, and switch apiary size to see how variance between hives shows up in the aggregate.
Tracking just a handful of consistent numbers per hive visit — rather than free-text notes — is usually enough to catch a failing queen or a mite spike weeks before it becomes a dead-out.