\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Extracting the height of lettuce by using neural networks of image
recognition in deep learning}



\author[1]{Minjuan Wang}%
\author[1]{Xiyue Guo}%
\author[1]{Yong Zhong}%
\author[1]{Yarong Feng}%
\author[1]{Ming Zhao}%
\author[1]{Minjuan Wang}%
\affil[1]{China Agricultural University}%


\vspace{-1em}



  
  \date{May 21, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
The traditional method of measuring the lettuce height is a manual
measurement with instruments, which is greatly affected by human
error.At present, researchers have proposed to use color cameras to
obtain RGB images of lettuce, and to obtain the height of lettuce from
the images. However, these tasks usually require camera calibration or a
reference object with a known height, which is somewhat restrictive.
Considering that deep neural networks have a powerful ability to feature
extraction and expression, without camera calibration and reference
objects, we try to use four networks of image recognition to explore the
effect of deep learning on abstracting the lettuce height from RGB
images. On the test set, including 80 images and height from 0.9 cm to
7.5 cm, we achieve a good result with a mean absolute error of 1.22 mm.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

