Author

Jeremy Ruhter

University of Illinois Urbana-Champaign

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Plant Biology· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Research Focus:clustering· 3 Approach:deep learning· 3 Assay:imaging· 3 Research Focus:benchmarking· 2 Research Focus:dimensionality reduction· 2 Organism:plant (other)· 2 Assay:fluorescence / live-cell microscopy· 1

Frequent Co-Authors

Andrew D. B. LeakeyiD· 3 shared Sebastián VarelaiD· 3 shared Dylan P. AlleniD· 2 shared Erik J. SacksiD· 2 shared Xuying ZhengiD· 2 shared Anna Hale· 1 shared Ashish B. RajurkariD· 1 shared Blake Long· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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3 shared papersPlant Biology

Andrew D. B. LeakeyORCID

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3 papers

Recent·Most cited

PhenoStream: A Cyberinfrastructure for Automated and AI-Based Crop Trait Extraction from Aerial Imagery

Sebastián Varela, Jeremy Ruhter, Erik J. Sacks, Xuying Zheng, Dylan Allen, Anna Hale, Cory Landry, Xianyan Kuang, Blake Long, Ernst Cebert, Yan Zhu, Shatabdi Deb Proma, Sehijpreet Kaur, Diego Jarquín, Jesse I. Morrison, Andrew D. B. Leakey

PhenoStream automates crop trait extraction from aerial imagery, enabling high-throughput phenotyping with reduced latency and manual effort.

bioRxiv · Aug 30, 2026 · PreprintPlant Biology

ROOTQUANT: AUTOMATED ROOT TRAIT QUANTIFICATION FROMMINIRHIZOTRON IMAGES USING DEEP LEARNING

Kinjalk Parth, Sebastián Varela, Zhiru Liu, K. Michael Martini, Ashish B. Rajurkar, Dylan P. Allen, Scott M. McCoy, Jeremy Ruhter, Samuel Walker, Nigel D. Goldenfeld, Andrew D. B. Leakey

RootQuant uses deep learning to automatically quantify root traits from minirhizotron images, achieving high accuracy across species without pixel-level annotations.

bioRxiv · Jul 8, 2026 · PreprintPlant Biology

Breaking the barrier of human-annotated training data for machine-learning-aided plant research using aerial imagery

Sebastián Varela, Xuying Zheng, Joyce Njoki Njuguna, Erik J. Sacks, Dylan P. Allen, Jeremy Ruhter, Andrew D. B. Leakey

A machine learning approach accelerates plant phenotyping by reducing the need for large amounts of human-annotated data, enabling faster and more efficient detection of a plant trait using aerial imagery.

PLANT PHYSIOLOGY · Mar 28, 2025Peer-reviewedBioinformatics