Atkins Kieran, Garzón-Martínez Gina A, Lloyd Andrew, Doonan John H, Lu Chuan
National Plant Phenomics Centre, IBERS, Aberystwyth University, Aberystwyth SY23 3EE, UK.
Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Mosquera, Cundinamarca, 250047, Colombia.
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giae123.
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.
深度学习可以通过自动测量复杂性状来彻底改变基于高通量图像的表型分析，而这一任务通常劳动强度大、耗时且容易出现人为误差。然而，其在准确对器官水平性状（如果实形态）进行表型分析方面的精度和适应性仍有待全面评估。建立表型变异与基因型变异之间的联系对于揭示性状的遗传基础至关重要，也可以为管道有效性提供直系同源测试。在本研究中，我们使用来自多亲本高级世代杂交（MAGIC）作图群体的图像，评估深度学习在测量拟南芥果实形态变异方面的功效。我们训练了一个实例分割模型，并基于模型输出开发了一个对拟南芥果实形态进行表型分析的管道。我们的模型表现出色，检测的平均精度为88.0%，分割的平均精度为55.9%。对MAGIC群体的衍生表型指标进行数量性状基因座分析，确定了与果实形态相关的显著基因座。基于对332,194个单个果实的自动表型分析，该分析强调了深度学习作为对大群体进行表型分析的强大工具的能力。我们用于量化豆荚形态性状的管道具有可扩展性，并提供高质量的表型数据，有助于遗传分析和基因发现，以及推进作物育种研究。
