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\begin{document}

\title{Robust Phenotyping Robot with Tactile Feedback}



\author[1]{Ziling Chen}%
\author[1]{Xuan Li}%
\author[1]{Colin Young}%
\author[1]{Tianzhang Zhao}%
\author[1]{Clayton Ness}%
\author[1]{Nathanael Jahn Elkin}%
\author[1]{Santiago Canova}%
\author[1]{Maximus Timothy Shurr}%
\author[1]{Raghava Sai Uppuluri}%
\author[1]{Yu She}%
\author[1]{Jian Jin}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 20, 2024}


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\begin{abstract}
Modern agriculture increasingly relies on non-invasive techniques for
plant health monitoring. Touch-based proximal hyperspectral imaging
(HSI), where sensors directly clamp onto leaves, offers intricate plant
details but faces distinct challenges. Current methods lack real-time
awareness of the pressure and alignment between the sensor and the leaf
surface. This blind interaction could lead to potential damage to the
leaf or even affect internal cellular structures, which could alter the
hyperspectral readings. In this research, we introduce the integration
of a tactile sensor with a robotic system specialized for touch-based
proximal HSI of corn. This fusion of HSI with the GelSight sensor
provides high-resolution tactile feedback, crucial for gentle plant
navigation and precise positioning. This tactile guidance ensures the
robotic system applies consistent and safe pressure on the corn,
reducing the risk of unintended damage and improving the consistency in
hyperspectral data. Experiments showcased the efficacy of this
integration. Results highlighted a significant improvement in imaging
data quality, scanning success rate, and consistency, with the tactile
sensor reducing inadvertent plant disturbances compared to non-tactile
methods. In conclusion, the seamless merger of HSI with tactile feedback
represents a significant advancement in precision agriculture. This
integrated approach promises enhanced plant health monitoring, fostering
sustainable farming and increased yield potential.%
\end{abstract}%



\sloppy


\emph{\textbf{Ziling Chen\textsuperscript{1}}, Xuan
Li\textsuperscript{1}, Colin Young\textsuperscript{1}, Tianzhang
Zhao\textsuperscript{1}, Clayton Ness\textsuperscript{1}, Nathanael
Jahn} \emph{Elkin\textsuperscript{1},}

\emph{Santiago} \emph{Canova\textsuperscript{1},} \emph{Maximus Timothy
Shurr\textsuperscript{2}, Raghava Sai Uppuluri\textsuperscript{3}, Yu
She\textsuperscript{3}, Jian Jin\textsuperscript{1}}

\emph{\textsuperscript{1}Agricultural \& Biological Engineering, Purdue
University, West Lafayette, USA}

\emph{\textsuperscript{2}Mechanical Engineering, Purdue University, West
Lafayette, USA}

\emph{\textsuperscript{3}Industrial Engineering, Purdue University, West
Lafayette, USA}

\textbf{ORCiD:} 0000-0003-4060-6499

\textbf{Keywords:} phenotying, robotic arm, corn, hyperspectral imaging,
proximal sesning, gelsight, precision agriculture

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