Nodules form on plant roots through the symbiotic relationship between soybean (Glycine max L. Merr.) roots and bacteria (Bradyrhizobium japonicum), and are an important structure where atmospheric nitrogen (N2) is fixed into bio-available ammonia (NH3) for plant growth and developmental. Nodule quantification on soybean roots is a laborious and tedious task; therefore, assessment is done on a less informative qualitative scale. We report the Soybean Nodule Acquisition Pipeline (SNAP) for nodule quantification that combines RetinaNet and UNet deep learning architectures for object (i.e., nodule) detection and segmentation. SNAP was built using data from 691 unique roots from diverse soybean genotypes, vegetative growth stages, and field locations; and has a prediction accuracy of 99%. SNAP reduces the human labor and inconsistencies of counting nodules, while acquiring quantifiable traits related to nodule growth, location and distribution on roots. The ability of SNAP to phenotype nodules on soybean roots at higher throughput enables researchers to assess the genetic and environmental factors, and their interactions on nodulation from an early development stage. The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficient soybean and other legume species cultivars, as well as enhanced insight into the plant-Bradyrhizobium relationship.

中文翻译：

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使用机器学习开发全自动大豆根瘤收购管道（SNAP）

根瘤是通过大豆根与细菌根瘤菌之间的共生关系在植物根上形成的，并且是大气氮（N2）固定在植物的生物可利用氨（NH3）中的重要结构。成长与发展。在大豆根上进行根瘤定量是一项艰巨而繁琐的任务。因此，评估的信息量较少。我们报告了大豆结节采集管道（SNAP）进行结节量化，结合了RetinaNet和UNet深度学习架构进行对象（即结节）检测和分割。SNAP是使用来自691种不同大豆基因型，营养生长阶段和田间位置的独特根的数据构建的。并具有99％的预测准确性。SNAP减少了人工工作，减少了结节计数的不一致性，同时获得了与根瘤生长，根部位置和分布有关的可量化特征。SNAP在大豆根上以较高通量表型结节的能力使研究人员能够评估遗传和环境因素，以及它们从早期发育阶段在结瘤时的相互作用。SNAP在研究和育种管道中的应用可能会导致更多的氮利用效率更高的大豆和其他豆科植物品种的栽培，并增强对植物-根瘤菌关系的了解。以及它们从早期开发阶段就结瘤的相互作用。SNAP在研究和育种管道中的应用可能会导致更多的氮利用效率高的大豆和其他豆类物种栽培品种，并增强对植物-根瘤菌关系的了解。以及它们从早期开发阶段就结瘤的相互作用。SNAP在研究和育种管道中的应用可能会导致更多的氮利用效率更高的大豆和其他豆科植物品种的栽培，并增强对植物-根瘤菌关系的了解。