Samplify: a versatile tool for image-based segmentation and annotation of seed abortion phenotypes.

Abstract

Automated seed phenotyping has wide applications in research and agriculture and relies on easy-to-use platforms and pipelines. Seed phenotyping in the model species Arabidopsis thaliana poses a significant challenge due to the large number of tiny seeds produced by individual plants, which are difficult to manually separate and count. Manual counting methods are time-consuming and prone to user bias, particularly for subtle phenotypic changes. To address these limitations, we developed Samplify, a scalable, automated pipeline for seed segmentation and classification by integrating classical image processing techniques with Meta's Segment Anything Model. Samplify effectively segments Arabidopsis seeds, even in dense clusters where conventional methods fail. To demonstrate its versatility, we quantified the seed abortion occurring in interploidy crossings in Arabidopsis, often referred to as 'triploid block'. Samplify includes a random forest classifier trained on a set of computed seed shape features that enable the categorization of seeds into normal, partially collapsed, and fully collapsed seeds, automating the manual classification process. The tool, designed as a command-line application, significantly reduces manual annotation workload. Our validation across multiple datasets demonstrates high segmentation and classification reliability, making Samplify a valuable resource for the plant research community.

Plant phenotyping relevance

種子画像のセグメンテーション・分類による表現型抽出パイプラインを開発し、複数データセットで信頼性を検証しているため、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。

No evidence-backed public reproduction asset is currently recorded.