# Prediction error-driven memory consolidation for continual learning. On the case of adaptive greenhouse models

arXiv: https://arxiv.org/abs/2006.12616 · HTML: https://mlsanity.com/paper/2006.12616/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 5 of 5)

- [IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control](https://mlsanity.com/paper/2107.05464/) (2021)
- [CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation](https://mlsanity.com/paper/2401.08940/) (2024)
- [Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification](https://mlsanity.com/paper/2606.03292/) (2026)
- [Challenges of Explainability in Continual Learning for Time Series Forecasting](https://mlsanity.com/paper/2607.19382/) (2026)
- [Attention-based Experience Replay Framework for Continual Learning of Agnostic Time Series Forecasting Models](https://mlsanity.com/paper/2607.20493/) (2026)

## Abstract (arXiv)

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.