Using Machine Learning Enabled Phenotyping To Characterize Nodulation In Three Early Vegetative Stages In Soybean

Abstract

The symbiotic relationship between soybean [Glycine max L. (Merr.)] roots and bacteria (Bradyrhizobium japonicum) lead to the development of nodules, important legume root structures where atmospheric nitrogen (N2) is fixed into bio-available ammonia (NH3) for plant growth and development. With the recent development of the Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybean root system architectures. We explored six diverse soybean genotypes across three field year combinations in three early vegetative stages of development and report the unique relationships between soybean nodules in the taproot and non-taproot growth zones of diverse root system architectures of these genotypes. We found unique growth patterns in the nodules of taproots showing genotypic differences in how nodules grew in count, size, and total nodule area per genotype compared to non-taproot nodules. We propose that nodulation should be defined as a function of both nodule count and individual nodule area resulting in a total nodule area per root or growth regions of the root. We also report on the relationships between the nodules and total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findings could lead to an enhanced understanding of the plant-Bradyrhizobium relationship, and exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybean germplasm.

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

“…RSA Fingerprints would further enable a whole plant analysis and efficient query system, and technology such as Xray-CT already enables dense 3D point clouds to be built of RSA ( Gerth et al., 2021 ; Teramoto et al, 2021 ). Whole plant fingerprints could help meet the need for efficient RSA and canopy modeling, clustering, and assessment ( Falk et al., 2020a ; Carley et al., 2022a ) while further exploring the root and shoot relationships to critical traits such as nodulation ( Carley et al, 2022b ). Irrespective of shoot or root fingerprints, there is tremendous potential for using this information to ID specific accessions and characterize germplasm collection ( Azevedo Peixoto et al., 2017 ), cluster them based on their canopy features, develop relationships between agronomic, disease, or stress-induced traits, and modularize canopy features for their integration in trait development.…”

Leveraging Soil Mapping and Machine Learning to Improve Spatial Adjustments in Plant Breeding Trials

“…As previously explained, soil mapping and interpretable spatial adjustments can be useful to study abiotic and biotic stress responses particularly in conjunction with HTP and ML [71][72][73]. Furthermore, advances in root trait studies [23,24,[74][75][76] can be complemented with the use of soil maps and spatial adjustments for a more holistic interpretation of plant response accounting for both above-and below ground traits.…”