Root and Microbiome studies

A.K. Singh’s research program focuses on integrating root system architecture phenotyping with microbiome profiling to enhance nutrient acquisition, stress resilience, and yield stability in soybean. By employing techniques like field “shovelomics” and computer vision, the team quantifies root architecture, facilitating genome-to-phenome analysis and supporting GWAS for stress-related traits to develop resilient cultivars. The program investigates how root traits influence microbiome composition, especially beneficial taxa that improve nutrient use and stress mitigation. Projects aim to optimize root traits and microbiome functions while researching the role of roots with the aim to optimize the ideotype.

Papers of interest:

• Carley CN, MJ Zubrod, S Dutta, AK Singh. (2022). Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean. Crop Science, 00, 1– 23. https://doi.org/10.1002/csc2.20861
• Jubery TZ,, CN Carley,, A Singh, S Sarkar, B Ganapathysubramanian, AK Singh. 2021. Using machine learning to develop a fully automated soybean nodule acquisition pipeline (SNAP). Plant Phenomics v2021, Article ID 9834746, 12 pages. DOI: 10.34133/2021/9834746
• Falk KG, TZ Jubery, JA O’Rourke, A Singh, S Sarkar, B Ganapathysubramanian, AK Singh. 2020. Soybean root system architecture traits study through genotypic, phenotypic and shape based clusters. Plant Phenomics. Article ID: 1925495. DOI: 10.34133/2020/1925495
• Falk KG,, T Jubery,, SV Mirnezami, KA Parmley, S Sarkar, A Singh, B Ganapathysubramanian, AK Singh. 2020. Computer Vision and Machine Learning Enabled Soybean Root Phenotyping Pipeline. BMC Plant Methods. v16, Article number: 5. https://doi.org/10.1186/s13007-019-0550-5