Christophe Périn

This paper develops a transformer-based deep learning pipeline to automate aerenchyma quantification in rice roots across environments, achieving expert-level accuracy and supporting climate-smart breeding.

The paper manually curates and annotates LRR-CR genes in rice genomes, improving annotation accuracy and proposing a new comprehensive annotation approach for future breeding applications.