Using Machine Learning To Develop A Fully Automated Soybean Nodule Acquisition Pipeline (SNAP)

Abstract

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 a 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.