Smith Abraham George, Malinowska Marta, Ruud Anja Karine, Janss Luc, Krusell Lene, Jensen Jens Due, Asp Torben
Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark.
Center for Quantitative Genetics and Genomics, Aarhus University, Slagelse 4200, Denmark.
AoB Plants. 2024 Sep 19;16(5):plae046. doi: 10.1093/aobpla/plae046. eCollection 2024 Oct.
Measuring seminal root angle is an important aspect of root phenotyping, yet automated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user-friendly we build on an established corrective annotation training method for image segmentation. We tested SeminalRootAngle on a heterogeneous dataset of 662 spring barley rhizobox images, which presented challenges in terms of image clarity and root obstruction. Validation of our new automated pipeline against manual measurements yielded a Pearson correlation coefficient of 0.71. We also measure inter-annotator agreement, obtaining a Pearson correlation coefficient of 0.68, indicating that our new pipeline provides similar root angle measurement accuracy to manual approaches. We use our new SeminalRootAngle tool to identify single nucleotide polymorphisms (SNPs) significantly associated with angle and length, shedding light on the genetic basis of root architecture.
测量种子根角度是根系表型分析的一个重要方面，但目前缺乏自动化方法。我们引入了SeminalRootAngle，这是一种新颖的开源自动化方法，可从图像中测量种子根角度。为确保我们的方法灵活且用户友好，我们基于一种已确立的用于图像分割的校正注释训练方法进行构建。我们在包含662张春季大麦根箱图像的异质数据集上测试了SeminalRootAngle，该数据集在图像清晰度和根系遮挡方面存在挑战。将我们新的自动化流程与手动测量结果进行验证，得到的皮尔逊相关系数为0.71。我们还测量了注释者之间的一致性，得到的皮尔逊相关系数为0.68，这表明我们新的流程提供的根角度测量精度与手动方法相似。我们使用新的SeminalRootAngle工具来识别与角度和长度显著相关的单核苷酸多态性（SNP），从而揭示根系结构的遗传基础。
