  Yield forecasting is a critical first step necessary for yield optimisation,
with important consequences for the broader food supply chain, procurement,
price-negotiation, logistics, and supply. However yield forecasting is
notoriously difficult, and oft-inaccurate. Premonition Net is a multi-timeline,
time sequence ingesting approach towards processing the past, the present, and
premonitions of the future. We show how this structure combined with
transformers attains critical yield forecasting proficiency towards improving
food security, lowering prices, and reducing waste. We find data availability
to be a continued difficulty however using our premonition network and our own
collected data we attain yield forecasts 3 weeks ahead with a a testing set
RMSE loss of ~0.08 across our latest season.
