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\begin{document}

\title{AICropCAM - An Edge Imaging Platform with a Stack of Deep Learning
Models for Plant Phenotyping}



\author[1]{Nipuna Chamara}%
\author[1]{Geng(Frank) Bai}%
\author[1]{Yufeng Ge}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
We propose a solution with edge image processing and long-range
connectivity named AICropCAM that can be used in drones, ground
platforms, or as distributed sensor networks for plant phenotyping. We
have successfully run multiple image classification, segmentation, and
object detection models on this platform. Classification models help
classify images based on image quality, crop type, and phenological
stage. Object detection models could detect and count the number of
plants, weeds, and insects and expand to count the flowers, fruits, and
leaves. Segmentation models can separate the canopy from the background
and potentially segment traits that indicate the nutrient deficit or
disease. Canopy segmentation results help estimate leaf area index and
chlorophyll content. Because the models run sequentially, like a
decision tree, there is flexibility to select the most accurate model
considering the crop type and the crop's phenological stage that helps
scan fields with multiple crops. The generated information is geo-tagged
and transmitted through low throughput long-range communication protocol
(e.g., LoRa) to cloud data storage. AICropCAM reduces 2-megabyte image
files to around 100-byte actionable data, resulting in massive savings
in data storage and transmission costs. This edge image capturing and
processing system is open to improvement with new neural network
predictive models and faster edge computers. This system provides plant
scientists and crop breeders a low-cost, flexible phenotyping tool to
extract multiple crop traits related to abiotic and biotic stress
responses.%
\end{abstract}%



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\emph{\textbf{Nipuna Chamara}\textsuperscript{1}, Geng (Frank)
Bai\textsuperscript{1}, Yufeng Ge\textsuperscript{1,2}}

\emph{\textsuperscript{1}Department of Biological Systems Engineering,
University of Nebraska-Lincoln, Lincoln, NE 68583, USA}

\emph{\textsuperscript{2}Center for Plant Science Innovation, University
of Nebraska-Lincoln, Lincoln, NE 68588, USA}

\textbf{ORCiD:} {[}0000-0002-0574-9016{]}

\textbf{Keywords:} Edge Computing, Plant Phenotyping, Crop Scouting,
Artificial Intelligence, Precision Agriculture, Internet of Things,
Sensor Networks. Phenotyping Platforms

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