Transformer-Based Phenotyping of Rice Root Aerenchyma Across Environments Enables Climate-Smart Rice Selection

Abstract

ABSTRACT Quantification of root anatomical traits such as cortical aerenchyma is key to understanding rice adaptation to diverse water regimes. Recently, the role of aerenchyma in regulating methane emissions has been demonstrated, making it a target for climate change mitigation. Despite its importance, breeding for root anatomical traits remains limited because manual analysis of root cross-sections is labor-intensive, inconsistent, and poorly scalable, and analysis pipelines do not generalize across heterogeneous imaging conditions. We present a deep learning pipeline based on a recent vision transformer architecture to automatically segment rice root anatomical structures and quantify aerenchyma. The model was trained on a multi-environment dataset of 1,760 annotated rice root cross-sections acquired across growth stages, cultivation systems, and countries, using a collaboratively defined annotation protocol. The model achieved high segmentation performance (mean Intersection-over-Union > 0.92) and near-perfect aerenchyma ratio quantification (R 2 = 0.98), and was evaluated by two experts as performing on par with, and in some cases better than, expert annotators. Delivered as open-source software with an online interactive demonstrator, the pipeline revealed differences in aerenchyma across genotypes, water regimes, environments, and developmental stages. Overall, this work demonstrates that transformer-based segmentation enables high-throughput anatomical phenotyping, supporting scalable and climate-smart rice breeding. HIGHLIGHTS Transformer-based segmentation enables robust aerenchyma phenotyping across environments A SegFormer model achieves expert-level accuracy on diverse rice root cross-sections Automated analysis delivers near-perfect lacuna-to-cortex ratio quantification (R 2 ≈ 0.98) Our online demonstrator supports scalable, climate-smart rice breeding applications

Plant phenotyping relevance

イネ根の画像から通気組織を自動分割・定量する深層学習パイプラインを開発し、異なる環境で性能検証した、中心的な植物フェノタイピング研究である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.