GenYOLO-leaf: a data-centric and open source framework for generalizable leaf instance segmentation across diverse datasets

Abstract

Abstract Ensuring plant sustainability is critically important across numerous domains. Specifically, the detection and segmentation of leaves are essential for tasks such as identifying plant diseases, monitoring plant growth, and determining plant phenotypes. However, the limited diversity in both data and species within existing datasets prepared for instance segmentation tasks often leads to the development of models with poor generalization capabilities and significant biases. This study introduces GenYOLO-Leaf, a data-centric, open-source framework designed to address these limitations. GenYOLO-Leaf facilitates instance-based leaf segmentation with improved generalization capabilities using different data sets with enriched label information by extracting approximately 145K leaf instances. It can also serve as a valuable resource for transfer learning across various segmentation tasks. The developed framework underwent zero-shot evaluation using a total of eight distinct datasets: four for instance segmentation and four for semantic segmentation. Experimental results indicate that the framework achieved mAP scores ranging from 62 to 84% on instance segmentation datasets, while producing mean IoU scores between 86 and 99% on semantic segmentation datasets. The GenYOLO-Leaf framework that includes model weights for YOLOv11 and YOLOv8 is publicly available at https://github.com/aaslihanyildirim/GenYOLO-Leaf .

Plant phenotyping relevance

葉のインスタンスセグメンテーションを汎用的な植物形質抽出に用いるオープンソース枠組みを開発し、複数データセットで性能評価しているため、植物フェノタイピング手法が中心である。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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