Improving fishing ground estimation with weak supervision and meta-learning.

ABSTRACT: Estimating fishing grounds is an important task in the fishing industry. This study modeled the fisher's decision-making process based on sea surface temperature patterns as a pattern recognition task. We used a deep learning-based keypoint detector to estimate fishing ground locations from these patterns. However, training the model required catch data for annotation, the amount of which was limited. To address this, we proposed a training strategy that combines weak supervision and meta-learning to estimate fishing grounds. Weak supervision involves using partially annotated or noisy data, where the labels are incomplete or imprecise. In our case, catch data cover only a subset of fishing grounds, and trajectory data, which are readily available and larger in volume than catch data, prov

SUBMITTER: Takasan K 

PROVIDER: S-EPMC11991730 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature