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 (SAM), 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 enables the categorization of seeds into normal, partially aborted, and fully aborted 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

種子の画像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検証しており、方法論が研究の中心である。

Code and data availability

The paper describes Samplify, a seed segmentation/classification pipeline with a trained Random Forest model and manually annotated training images, but the supplied blocks contain no public deposit, availability statement, or authors' URL for the code, model, or image datasets. Assets would need to be requested from (

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