Schooling fish feeding desire evaluation method and device coupling adaptive forgetting learning and deep learning

Abstract

This invention discloses a method and apparatus for assessing fish feeding desire by coupling adaptive forgetting learning and deep learning. The method includes: collecting videos of fish during rearing and feeding periods to train a recurrent neural network (RNN) model for assessing fish feeding desire; optimizing the model using adaptive forgetting learning technology, which mainly includes three innovative technologies: an adaptive label flipping strategy, a dual-gradient correction mechanism, and multi-round forgetting cumulative error analysis and control. This allows the model to forget previously collected but outdated training data without needing to retrain from scratch, effectively improving the accuracy and self-updating adaptive capability of the fish feeding desire assessment model. This invention is precise, simple, and efficient, with a simple device structure. It effectively solves the problem of decreased accuracy in current aquaculture feeding desire assessment models due to outdated data, ensuring the model's self-adaptive and self-optimizing performance over long-term operation, thus facilitating intelligent aquaculture management.

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