  This work presents an adaptive architecture that performs online learning and
faces catastrophic forgetting issues by means of episodic memories and
prediction-error driven memory consolidation. In line with evidences from the
cognitive science and neuroscience, memories are retained depending on their
congruency with the prior knowledge stored in the system. This is estimated in
terms of prediction error resulting from a generative model. Moreover, this AI
system is transferred onto an innovative application in the horticulture
industry: the learning and transfer of greenhouse models. This work presents a
model trained on data recorded from research facilities and transferred to a
production greenhouse.
