Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis

Abstract

Abstract Deep learning can revolutionise high-throughput image-based phenotyping by automating the measurement of complex traits, a task that is often labour-intensive, time-consuming, and prone to human error. However, its precision and adaptability in accurately phenotyping organ-level traits, such as fruit morphology, remain to be fully evaluated. Establishing the links between phenotypic and genotypic variation is essential for uncovering the genetic basis of traits and can also provide an orthologous test of pipeline effectiveness. In this study, we assess the efficacy of deep learning for measuring variation in fruit morphology in Arabidopsis using images from a multiparent advanced generation intercross (MAGIC) mapping family. We trained an instance segmentation model and developed a pipeline to phenotype Arabidopsis fruit morphology, based on the model outputs. Our model achieved strong performance with an average precision of 88.0% for detection and 55.9% for segmentation. Quantitative trait locus analysis of the derived phenotypic metrics of the MAGIC population identified significant loci associated with fruit morphology. This analysis, based on automated phenotyping of 332,194 individual fruits, underscores the capability of deep learning as a robust tool for phenotyping large populations. Our pipeline for quantifying pod morphological traits is scalable and provides high-quality phenotype data, facilitating genetic analysis and gene discovery, as well as advancing crop breeding research.

Plant phenotyping relevance

深層学習による果実形態の画像ベース表現型抽出パイプラインを開発し、検出・セグメンテーション性能を評価しており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The paper's phenotyping pipeline (MorphPod/silique-detector), its annotation tool (GIMP Image Annotator), versioned releases, a Software Heritage archive, and DOME-ML registry annotations are publicly available with explicit availability statements. The Zenodo phenotype/genotype dataset is referenced but its URL is not