我正在尝试实现类似以下文章中的表格的功能： https://www.sciencedirect.com/science/article/pii/S0168169917308803
这是一份两栏的 Elsevier 评论，其中包含一些长表格，用于显示已审阅的参考文献和一些描述。我尝试使用 pdflscape、rotatebox、longtable、supertabular、tabularx，但无法使其工作。我正在使用 Overleaf 在线编辑器，我相信我可以从工作示例中自己制作一个基本的水平长表格。非常感谢任何帮助！
答案1
选项1插入适合页面的横向表格。
此示例显示一个旋转后的表格，该表格填满了页面的可用空间。它被收集在框中并旋转。
必须手动填充表格以适合页面，因为是浮点数且无法在框中工作，所以longtable不能使用自动分页符。longable
\documentclass[final,3p,twocolumn,authoryear]{elsarticle}
\usepackage{graphicx} % rotatebox
\usepackage{array}
\newcolumntype{L}[1]{>{\footnotesize\raggedright \arraybackslash}p{#1}}
\newlength{\twidth}
\setlength{\twidth}{\dimexpr \textheight+\headheight+\topsep} % total usable page height
\usepackage{kantlipsum} % only for dummy text
%\usepackage{showframe} % use to show the margins
\journal{Computers and Electronics in Agriculture}
\begin{document}
\begin{frontmatter}
\title{The Title}
\author{One Author}
\affiliation{organization={},
addressline={},
city={},
postcode={},
state={},
country={}}
\begin{abstract}
%% Text of abstract
\end{abstract}
\end{frontmatter}
\section{Introduction }
1. \kant[1-4]
\clearpage
\rotatebox{90}{%
\hspace*{\dimexpr-0.8\columnwidth-\columnsep}\begin{minipage}{\textheight}
\setlength{\tabcolsep}{2pt}
\begin{tabular}{L{3ex} L{0.20\twidth} L{0.35\twidth}L{0.42\twidth}@{}}
No. & Application in Agriculture& Remote sensing& Techniques for data analysis\\
\hline
1.& Soil and vegetation/crop mapping& Hyperspectral imaging (satellite and airborne), multi‐spectral
imaging (satellite), synthetic aperture radar (SAR)& Image fusion, SVM, end-member extraction algorithm, co-polarized phase
differences (PPD), linear polarizations (HH, VV, HV), distance-based classification, decision trees, linear mixing models, logistic regression, ANN, NDVI\\
2.& Leaf area index and crop canopy& Hyperspectral imaging (airborne), multi‐spectral imaging (airborne) & Linear regression analysis, NDVI\\
3.& Crop phenology& Satellite remote sensing (general)& Wavelet-based filtering, Fourier transforms, NDVI\\
4.&
Crop height, estimation of yields,
fertilizers' effect and biomass&
Light Detection and Ranging (LIDAR), hyperspectral and multi-
spectral imaging, SAR, red-edge camera, thermal infrared&
Linear and exponential regression analysis, linear polarizations (VV),
wavelet-based filtering, vegetation indices (NDVI, ICWSI), ANN\\
5.&
Crop monitoring&
Satellite remote sensing (hyperspectral and multi-spectral imaging),
NIR camera, SAR&
Stepwise discriminate analysis (DISCRIM) feature extraction, linear
regression analysis, co-polarized phase differences (PPD), linear polarizations
(HH, VV, HV, RR and RL), classification and regression tree analysis\\
6.&
Identification of seeds and
reorganization of species&
Remote sensing in general, cameras and photo-detectors,
hyperspectral imaging&
Principal component analysis, feature extraction, linear regression analysis\\
7.&
Soil and leaf nitrogen content and
treatment, salinity detection&
Hyperspectral and multi-spectral imaging, thermal imaging&
Linear and exponential regression analysis\\
8.&
Irrigation&
Satellite remote sensing (hyperspectral and multi-spectral imaging),
red-edge camera, thermal infrared&
Image classification techniques (unsupervised clustering, density slicing with
thresholds), decision trees, linear regression analysis, NDVI\\
9.&
Plants water stress detection and drought
conditions&
Satellite remote sensing (hyperspectral and multi-spectral imaging,
radar images), thermal imaging, NIR camera, red-edge camera&
Fraunhofer Line Depth (FLD) principle, linear regression analysis, NDVI\\
10.&
Water erosion assessment&
Satellite remote sensing (optical and radar images), SAR, NIR
camera&
Interferometric SAR image processing, linear and exponential regression
analysis, contour tracing, linear polarizations (HH, VV)\\
11.&
Pest detection and management&
Hyperspectral and multi-spectral imaging, microwave remote
sensing, thermal camera&
Image processing using sample imagery, linear and exponential regression
analysis, statistical analysis, CEM nonlinear signal processing, NDVI\\
12.&
Weed detection&
Remote sensing in general, optical cameras and photo-detectors,
hyperspectral and multi-spectral imaging&
Pixel classification based on k-means clustering and Bayes classifier, feature
extraction techniques with FFT and GLCM, wavelet-based classification and
Gabor filtering, genetic algorithms, fuzzy techniques, logistic regression, edge
detection, color detection, principal component analysis\\
13.&
Herbicide&
Remote sensing in general, optical cameras and photo-detectors&
Fuzzy techniques, discriminant analysis\\
14.&
Fruit grading&
Optical cameras and photo-detectors, monochrome images with
different illuminations&
K-means clustering, image fusion, color histogram techniques, machine
learning (esp. SVM), Bayesian discriminant analysis, Bayes filtering, linear
discriminant analysis\\
15.&
Packaged food and food products –
identification of contaminants, diseases
or defects, bruise detection&
X-ray imaging (or transmitted light), CCD cameras, monochrome
images with different illuminations, thermal cameras, multi-spectral
and hyperspectral NIR-based imaging&
3D vision, invariance, pattern recognition and image modality,
decision trees, fusion, feature extraction techniques with FFT, standard
Bayesian discriminant analysis, feature analysis, color, shape and geometric
features using discrimination analysis, pulsed-phase thermography\\
16.&
Crop hail damage&
Multi-spectral imaging, polarimetric radar imagery&
Linear and exponential regression analysis, unsupervised image classification\\
17.&
Agricultural expansion and
intensification&
Satellite remote sensing in general&
Wavelet-based filtering\\
18.&
Greenhouse monitoring&
Optical and thermal cameras&
Linear and exponential regression analysis, unsupervised classification,
NDVI, IR thermography\\
\end{tabular}
\end{minipage}
}
\clearpage
2. \kant[4-6]
\end{document}
选项 2longtable 当表真的很长时 使用。
需要先生成长表，然后将生成的pdf插入到elsarticle
（1）使用此代码longtablelandscape.tex生成longtablelandscape.pdf，长三页。
% File longtablelandscape.tex
\documentclass{article}
\RequirePackage{geometry}
\geometry{%
paperwidth=210mm,
paperheight=297mm,
textheight=622pt,
textwidth=468pt,
centering,
headheight=10pt,
headsep=12pt,
footskip=12pt,
footnotesep=14pt plus 2pt minus 12pt,
}
\RequirePackage{pdflscape}
\RequirePackage{afterpage}
\RequirePackage{longtable}
\RequirePackage{caption}
\RequirePackage{array}
\begin{document}
\pagestyle{empty}
\input{landscape_template}
\end{document}
longtable此文件中的名为landscape_template.tex
%%% File landscape_template.tex
\newlength{\twidth}
\setlength{\twidth}{\dimexpr \textheight+\headheight+\topsep} % total usable page height
\newcolumntype{L}[1]{>{\small\raggedright \arraybackslash}p{#1}}
\afterpage{%
\begin{landscape}% Landscape page
\appendix
\section{Application in Agriculture}
\begin{longtable}{L{3ex} L{0.20\twidth} L{0.35\twidth}L{0.42\twidth}@{}}
% \caption{\Large Dimension I: Transcendental logic} \label{D1} \\
No. & Application in Agriculture& Remote sensing& Techniques for data analysis\\ \hline
\endfirsthead
1.& Soil and vegetation/crop mapping& Hyperspectral imaging (satellite and airborne), multi‐spectral
imaging (satellite), synthetic aperture radar (SAR)& Image fusion, SVM, end-member extraction algorithm, co-polarized phase
differences (PPD), linear polarizations (HH, VV, HV), distance-based classification, decision trees, linear mixing models, logistic regression, ANN, NDVI\\
2.& Leaf area index and crop canopy& Hyperspectral imaging (airborne), multi‐spectral imaging (airborne) & Linear regression analysis, NDVI\\
3.& Crop phenology& Satellite remote sensing (general)& Wavelet-based filtering, Fourier transforms, NDVI\\
4.&
Crop height, estimation of yields,
fertilizers' effect and biomass&
Light Detection and Ranging (LIDAR), hyperspectral and multi-
spectral imaging, SAR, red-edge camera, thermal infrared&
Linear and exponential regression analysis, linear polarizations (VV),
wavelet-based filtering, vegetation indices (NDVI, ICWSI), ANN\\
5.&
Crop monitoring&
Satellite remote sensing (hyperspectral and multi-spectral imaging),
NIR camera, SAR&
Stepwise discriminate analysis (DISCRIM) feature extraction, linear
regression analysis, co-polarized phase differences (PPD), linear polarizations
(HH, VV, HV, RR and RL), classification and regression tree analysis\\
6.&
Identification of seeds and
reorganization of species&
Remote sensing in general, cameras and photo-detectors,
hyperspectral imaging&
Principal component analysis, feature extraction, linear regression analysis\\
7.&
Soil and leaf nitrogen content and
treatment, salinity detection&
Hyperspectral and multi-spectral imaging, thermal imaging&
Linear and exponential regression analysis\\
8.&
Irrigation&
Satellite remote sensing (hyperspectral and multi-spectral imaging),
red-edge camera, thermal infrared&
Image classification techniques (unsupervised clustering, density slicing with
thresholds), decision trees, linear regression analysis, NDVI\\
9.&
Plants water stress detection and drought
conditions&
Satellite remote sensing (hyperspectral and multi-spectral imaging,
radar images), thermal imaging, NIR camera, red-edge camera&
Fraunhofer Line Depth (FLD) principle, linear regression analysis, NDVI\\
10.&
Water erosion assessment&
Satellite remote sensing (optical and radar images), SAR, NIR
camera&
Interferometric SAR image processing, linear and exponential regression
analysis, contour tracing, linear polarizations (HH, VV)\\
11.&
Pest detection and management&
Hyperspectral and multi-spectral imaging, microwave remote
sensing, thermal camera&
Image processing using sample imagery, linear and exponential regression
analysis, statistical analysis, CEM nonlinear signal processing, NDVI\\
12.&
Weed detection&
Remote sensing in general, optical cameras and photo-detectors,
hyperspectral and multi-spectral imaging&
Pixel classification based on k-means clustering and Bayes classifier, feature
extraction techniques with FFT and GLCM, wavelet-based classification and
Gabor filtering, genetic algorithms, fuzzy techniques, logistic regression, edge
detection, color detection, principal component analysis\\
13.&
Herbicide&
Remote sensing in general, optical cameras and photo-detectors&
Fuzzy techniques, discriminant analysis\\
14.&
Fruit grading&
Optical cameras and photo-detectors, monochrome images with
different illuminations&
K-means clustering, image fusion, color histogram techniques, machine
learning (esp. SVM), Bayesian discriminant analysis, Bayes filtering, linear
discriminant analysis\\
15.&
Packaged food and food products –
identification of contaminants, diseases
or defects, bruise detection&
X-ray imaging (or transmitted light), CCD cameras, monochrome
images with different illuminations, thermal cameras, multi-spectral
and hyperspectral NIR-based imaging&
3D vision, invariance, pattern recognition and image modality,
decision trees, fusion, feature extraction techniques with FFT, standard
Bayesian discriminant analysis, feature analysis, color, shape and geometric
features using discrimination analysis, pulsed-phase thermography\\
16.&
Crop hail damage&
Multi-spectral imaging, polarimetric radar imagery&
Linear and exponential regression analysis, unsupervised image classification\\
17.&
Agricultural expansion and
intensification&
Satellite remote sensing in general&
Wavelet-based filtering\\
18.&
Greenhouse monitoring&
Optical and thermal cameras&
Linear and exponential regression analysis, unsupervised classification,
NDVI, IR thermography\\
19.& Soil and vegetation/crop mapping& Hyperspectral imaging (satellite and airborne), multi‐spectral
imaging (satellite), synthetic aperture radar (SAR)& Image fusion, SVM, end-member extraction algorithm, co-polarized phase
differences (PPD), linear polarizations (HH, VV, HV), distance-based classification, decision trees, linear mixing models, logistic regression, ANN, NDVI\\
20.& Leaf area index and crop canopy& Hyperspectral imaging (airborne), multi‐spectral imaging (airborne) & Linear regression analysis, NDVI\\
21.& Crop phenology& Satellite remote sensing (general)& Wavelet-based filtering, Fourier transforms, NDVI\\
22.&
Crop height, estimation of yields,
fertilizers' effect and biomass&
Light Detection and Ranging (LIDAR), hyperspectral and multi-
spectral imaging, SAR, red-edge camera, thermal infrared&
Linear and exponential regression analysis, linear polarizations (VV),
wavelet-based filtering, vegetation indices (NDVI, ICWSI), ANN\\
23.&
Crop monitoring&
Satellite remote sensing (hyperspectral and multi-spectral imaging),
NIR camera, SAR&
Stepwise discriminate analysis (DISCRIM) feature extraction, linear
regression analysis, co-polarized phase differences (PPD), linear polarizations
(HH, VV, HV, RR and RL), classification and regression tree analysis\\
24.&
Identification of seeds and
reorganization of species&
Remote sensing in general, cameras and photo-detectors,
hyperspectral imaging&
Principal component analysis, feature extraction, linear regression analysis\\
25.&
Soil and leaf nitrogen content and
treatment, salinity detection&
Hyperspectral and multi-spectral imaging, thermal imaging&
Linear and exponential regression analysis\\
26.&
Irrigation&
Satellite remote sensing (hyperspectral and multi-spectral imaging),
red-edge camera, thermal infrared&
Image classification techniques (unsupervised clustering, density slicing with
thresholds), decision trees, linear regression analysis, NDVI\\
27.&
Plants water stress detection and drought
conditions&
Satellite remote sensing (hyperspectral and multi-spectral imaging,
radar images), thermal imaging, NIR camera, red-edge camera&
Fraunhofer Line Depth (FLD) principle, linear regression analysis, NDVI\\
28.&
Water erosion assessment&
Satellite remote sensing (optical and radar images), SAR, NIR
camera&
Interferometric SAR image processing, linear and exponential regression
analysis, contour tracing, linear polarizations (HH, VV)\\
29.&
Pest detection and management&
Hyperspectral and multi-spectral imaging, microwave remote
sensing, thermal camera&
Image processing using sample imagery, linear and exponential regression
analysis, statistical analysis, CEM nonlinear signal processing, NDVI\\
30.&
Weed detection&
Remote sensing in general, optical cameras and photo-detectors,
hyperspectral and multi-spectral imaging&
Pixel classification based on k-means clustering and Bayes classifier, feature
extraction techniques with FFT and GLCM, wavelet-based classification and
Gabor filtering, genetic algorithms, fuzzy techniques, logistic regression, edge
detection, color detection, principal component analysis\\
31.&
Herbicide&
Remote sensing in general, optical cameras and photo-detectors&
Fuzzy techniques, discriminant analysis\\
32.&
Fruit grading&
Optical cameras and photo-detectors, monochrome images with
different illuminations&
K-means clustering, image fusion, color histogram techniques, machine
learning (esp. SVM), Bayesian discriminant analysis, Bayes filtering, linear
discriminant analysis\\
33.&
Packaged food and food products –
identification of contaminants, diseases
or defects, bruise detection&
X-ray imaging (or transmitted light), CCD cameras, monochrome
images with different illuminations, thermal cameras, multi-spectral
and hyperspectral NIR-based imaging&
3D vision, invariance, pattern recognition and image modality,
decision trees, fusion, feature extraction techniques with FFT, standard
Bayesian discriminant analysis, feature analysis, color, shape and geometric
features using discrimination analysis, pulsed-phase thermography\\
34.&
Crop hail damage&
Multi-spectral imaging, polarimetric radar imagery&
Linear and exponential regression analysis, unsupervised image classification\\
35.&
Agricultural expansion and
intensification&
Satellite remote sensing in general&
Wavelet-based filtering\\
36.&
Greenhouse monitoring&
Optical and thermal cameras&
Linear and exponential regression analysis, unsupervised classification,
NDVI, IR thermography\\ \hline
\end{longtable}%
\end{landscape}%
}
（2）将长表插入主文档main.tex
% File main.tex
\documentclass[final,3p,twocolumn,authoryear]{elsarticle}
\usepackage[final]{pdfpages}
\RequirePackage{kantlipsum}
%\usepackage{showframe} % to show the margins
\journal{Computers and Electronics in Agriculture}
\begin{document}
\begin{frontmatter}
\title{The Title}
\author{One Author}
\affiliation{organization={},
addressline={},
city={},
postcode={},
state={},
country={}}
\begin{abstract}
%% Text of abstract
\end{abstract}
\end{frontmatter}
\section{Introduction }
1. \kant[1-4]
\onecolumn\includepdf[pages={-},width=\textwidth, angle=90, pagecommand={}]{longtablelandscape.pdf}
\twocolumn 2. \kant[2-6]
\end{document}
这是最终的输出：
