Landon Swartz

Landon Swartz

Computer Science Ph.D at University of Missouri

Publications

Segmentation of Arabidopsis thaliana Using Segment-Anything

Published in Proceedings of 2023 Applied Imagery Pattern Recognition Workshop, 2024

Here we present a new method for segmentation of plant using Segment-Anything and Grounded-Dino. A method of obtaining individual leaves was implemented to describe the advantages and limitations of the prompt-based method of deep learning models. An analysis of the segmentation results from Segment-Anything demonstrates that this is a powerful method for providing statistically valuable data for biological insights into novel plant traits under nutrient stress.

Recommended citation:

OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis

Published in The Plant Journal, 2023

Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability.

Recommended citation: Swartz, L.G., Liu, S., Dahlquist, D., Kramer, S.T., Walter, E.S., McInturf, S.A., Bucksch, A. and Mendoza-Cózatl, D.G. (2023), OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis. Plant J. https://doi.org/10.1111/tpj.16449 [http://academicpages.github.io/files/paper1.pdf](https://onlinelibrary.wiley.com/doi/10.1111/tpj.16449?af=R)