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\begin{document}

\title{Application of Transfer Learning for Root Segmentation in Assessment of Plant Health}



\author[1]{Justin Rossiter}%
\author[1]{Alina Zare}%
\author[1]{Todd M Palmer}%
\author[1]{Elizabeth G Pringle}%
\author[1]{Patrick Milligan}%
\affil[1]{Department of Electrical and Computer Engineering, Department of Biology, Department of Biology, University of Florida, University of Florida, University of Nevada}%


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  \date{May 24, 2024}


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\begin{abstract}
Minirhizotron imagery can be used to assess plant root health, and the
amount of data for analysis motivates automation of root detection
through use of neural networks. Building upon previous work, we show
that we can use transfer learning from our PRMI dataset to assess root
health across twelve classes of a new dataset to answer questions
regarding how root health is affected by access of a tree by large
herbivores, site infestation by Pheidole megacephala, and location of
the tree. This dataset was collected from three paired sites at the Ol
Pejeta Conservancy in Laikipia, Kenya, and consists of 20,000 images
collected between September 2021 and May 2022. Each paired site
represents four locations based on all four possible combinations of
site infestation by Pheidole megacephala for at least 20 years and
existence of herbivore-exclusion fence to keep large herbivores out.
1,332 images across all twelve classes of site and treatment combination
were labeled with respective ground truths for model training. Our work
uses the UNet architecture using pretrained weights on the network
encoder and decoder which were obtained in 2019 in work which achieved
over 99\% accuracy on a dataset of peanut and switchgrass imagery. In
our work, we found that training the model with our new dataset resulted
in consistent performance across all classes of our new dataset, with
over 99\% accuracy for each class.%
\end{abstract}%



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