Author

Richard D. Ashby

Agricultural Research ServiceORCID 0000-0002-3472-7545Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 1 Organism:fungi (other)· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Amelia Helen LovelaceiD· 1 shared Chansong Hwang· 1 shared Ezekiel Jin Sung AhniD· 1 shared Helen L. NgoiD· 1 shared Insuck BaekiD· 1 shared Lalit Mohan KandpaliD· 1 shared Lyndel W. MeinhardtiD· 1 shared M. S. Kim· 1 shared

Activity by Year

2026-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperMicrobiology

Amelia Helen LovelaceORCID

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1 paper

Recent·Most cited

Taxonomy-agnostic hyperspectral-morphological phenotyping of fungal pathogen chemical-stress responses using machine learning

Insuck Baek, Seunghyun Lim, Amelia Helen Lovelace, Sookyung Oh, Masoud Kazem‐Rostami, Helen L. Ngo, M. S. Kim, Lyndel W. Meinhardt, Lalit Mohan Kandpal, Minhyeok Cha, Chansong Hwang, Richard D. Ashby, Ezekiel Jin Sung Ahn

This paper develops a taxonomy-agnostic workflow using hyperspectral imaging and machine learning to predict fungal isolate crop-source labels with 86.7% accuracy.

Smart Agricultural Technology · Feb 17, 2026Peer-reviewedMicrobiology