Maui: modular analytics of UAS imagery for specialty crop research
We present a modular framework to efficiently extract spectral data for specialty crops from UAS imagery. We highlight two kinds of segmentation applied to trellised and row cropping systems to demonstrate the modularity and versatility of the proposed methodology. MAUI improved spectral discrimination between individual plants and treatment groups for hemp and grapevine, respectively. With the containerized deployment package and open-source codebase, MAUI can be widely adopted by specialty crop researchers to facilitate the integration of UAS imagery analysis into routine research.
Authors
- Lawrence B. Smart (ORCID: https://orcid.org/0000-0002-7812-7736)
- Kathleen Kanaley (ORCID: https://orcid.org/0000-0001-8625-253X)
- Kaitlin M. Gold (ORCID: https://orcid.org/0000-0002-0306-9244)
- Ertai Liu
- Maylin J Murdock (ORCID: https://orcid.org/0000-0002-5785-4010)
- Dominik Starzmann (ORCID: https://orcid.org/0000-0001-6857-7138)
- Schuyler E Seyram
- Jiang Yu (ORCID: https://orcid.org/0000-0002-4436-707X)
- Tian Qiu (ORCID: https://orcid.org/0000-0002-8608-5863)
Institutions
- University of Stuttgart (DE)
- Cornell University (US)
Publication Details
- Journal
- Plant Methods
- Published
- 2025-05-20
- DOI
- https://doi.org/10.1186/s13007-025-01376-7
- Citations
- 1
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- National Science Foundation
- U.S. Department of Agriculture
- New York State Department of Agriculture and Markets
- Foundation for Food and Agriculture Research
- Directorate for Biological Sciences
- National Institute of Food and Agriculture
- Cornell Institute for Digital Agriculture, Cornell University