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\begin{document}

\title{Field-based high throughput phenotyping of Fusarium head blight
in wheat using an RGB based deep neural network approach}



\author[1]{Julian Cooper}%
\author[1]{Chuan Du}%
\author[1]{Zach Beaver}%
\author[1]{Ming Zheng}%
\author[1]{Rae Page}%
\author[1]{Joseph R. Wodarek}%
\author[1]{Oadi Matny}%
\author[1]{Tamas Szinyei}%
\author[1]{Alejandra Quiñones}%
\author[1]{James A. Anderson}%
\author[1]{Kevin P. Smith}%
\author[1]{Ce Yang}%
\author[1]{Brian J. Steffenson}%
\author[1]{Cory D. Hirsch}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\begin{abstract}
Fusarium head blight (FHB) is an economically important disease in wheat
which can cause yield losses \textgreater{}50\%. Breeding for host
resistance is the most effective control method, however time, labor,
and human subjectivity limit phenotyping efforts. A novel,
high-throughput phenotyping rover was used to collect in-field RGB
images of inoculated wheat spikes at multiple time points in 2021 and
2022. A deep neural network pipeline was developed to classify wheat
spikes, segment healthy and diseased tissue, and quantify FHB severity
as the region of intersection between spike and disease masks. To
validate the pipeline, model inferences on a plot and spike scale were
compared to five raters who performed disease scoring in the field and
on images. The precision and throughput of the phenotyping rover and FHB
quantification pipeline exceeded conventional rating methods. The plot
aggregate disease scores based on pipeline outputs correlated strongly
with plot-level disease scores by raters in the field and imagery. When
comparing disease annotations on spike images, pipeline to human disease
correlations were equivalent to correlations between raters, however
location tended to influence disease assessment. The pipeline has strong
generalizability and performed well on images taken across environments,
with different camera orientations, and throughout disease progression.
These results demonstrate a breakthrough in FHB phenotyping and
facilitate precise and efficient disease quantification on spikes and
plot aggregates across time and imaging conditions that are unachievable
using conventional methods.%
\end{abstract}%



\sloppy


\textbf{Julian Cooper\textsuperscript{1}} , Chuan Du\textsuperscript{2},
Zach Beaver\textsuperscript{2}, Ming Zheng\textsuperscript{2}, Rae
Page\textsuperscript{1}, Joseph R. Wodarek\textsuperscript{3}, Oadi
Matny\textsuperscript{1}, Tamas Szinyei\textsuperscript{1}, Alejandra
Qui\selectlanguage{ngerman}ñones\textsuperscript{1}, James A. Anderson\textsuperscript{4}, Kevin
P. Smith\textsuperscript{4}, Ce Yang\textsuperscript{5}, Brian J.
Steffenson\textsuperscript{1}, Cory D. Hirsch\textsuperscript{1*}

\textsuperscript{1} Department of Plant Pathology, University of
Minnesota, Saint Paul, MN 55108

\textsuperscript{2} Mineral Earth Sciences LLC., Mountain View, CA 94043

\textsuperscript{3} Northwest Research and Outreach Center, University
of Minnesota, Crookston, MN 56716

\textsuperscript{4} Department of Agronomy and Plant Genetics,
University of Minnesota, Saint Paul, MN, 55108

\textsuperscript{5} Department of Bioproducts and Biosystems
Engineering, University of Minnesota, Saint Paul, MN 55108

\textbf{ORCiD:} 0000-0001-8390-3915

\textbf{Keywords:} Fusarium head blight, deep learning, neural network,
wheat spike, computer vision, disease segmentation, phenotyping rover

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