2025/09/21 by Heinrich Bente, Ronja Lea Jennifer Müller, Andreas Donath +2 · 1 voice
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Genetic Mapping and Diversity in Plants and Animals #Rice Cultivation and Yield Improvement #Wheat and Barley Genetics and Pathology
paper · doi:10.1101/2025.09.18.677122
openalex publication_date 2025/09/21 · openalex created_date 2025/09/26 · openalex updated_date 2026/07/14
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.