\documentclass[12pt]{elsarticle}
%% for removing the footer
\makeatletter
\def\ps@pprintTitle{%
\let\@oddhead\@empty
\let\@evenhead\@empty
%\let\@oddfoot\@empty
\let\@evenfoot\@oddfoot
}
\usepackage{multirow}
\usepackage{lineno} %% package for line number
%% The amssymb package provides various useful mathematical symbols
\usepackage{lscape}
\usepackage{graphicx}
\usepackage{subfig}
\usepackage{multicol}
\setlength{\columnsep}{1cm}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{textcomp}
\usepackage{amssymb}
\usepackage{url}
\usepackage{showframe}
\renewcommand\ShowFrameLinethickness{0.15pt}
\renewcommand*\ShowFrameColor{\color{red}}
\usepackage{afterpage,lscape}
\usepackage{lipsum, pdflscape}
\usepackage[noabbrev,nameinlink]{cleveref}
\usepackage[margin=25mm]{geometry}
\usepackage{graphicx}
\usepackage{tabularray}
\UseTblrLibrary{booktabs}
\begin{document}
\begin{table}
\caption{Sample Table}
\footnotesize\sffamily
\begin{tblr}{colsep= 3pt,
colspec={@{}r c X[2, l] X[1.5,l] X[l] X[l] @{}},
row{1} = {font=\bfseries, c, m},
rowsep= 3pt
}
\toprule
\textbf {Publishing \\date} & \textbf {Ref No.} & \textbf {Applied model} & \textbf {Dataset} & \textbf {Number of Outcomes (M)} & \textbf {Results (avg)} \\
\midrule
2011 & \cite{--------} & ABCDEFG;AAAAAA and AAAAAAA & FFFFFFFFFFFFFFFF & --- & 100\% \\
2011 & \cite{-------------} & Lightweight DCNN and Mask-RCNN & Greenhouse live TJ-Tomato and PlantVillage & --- & 100\% \\
2011 & \cite{---------} & DCNN & PlantVillage & 100 & 100\% \\
2011 & \cite{----------} & ResNet50; InceptionV2; MobileNetV1 & Deep transfer learning system dataset & --- & 100\% \\
2012 & \cite{-----------} & Concatenated residual networks (ComNet), VGGNet, AlexNet, ResNet, Xception, and MobileNet & PlantVillage and tea diseases dataset & 9.66 & 92.02\% and 86.17\% \\
2012 & \cite{-----------} & Pre-trained VGG-16, ResNet, GoogLeNet, Inception-v3, LeNet & Paddy field images & 14.81 & 95.89\% \\
2012 & \cite{-------------} & Hybrid DenseNet and Xception (XDNet); Inception-v3, MobileNet, VGG16, DenseNet201, Xception, VGG-INCEP & Apple leaf diseases & 10.16 & 95.82\% \\
2012 & \cite{--------} & DCNN, Inception-V3, ResNet-50, NasNet-Large, DenseNet-121 & Hydroponic experiments & 23.9; 26.7; 84.9; 8.1 & 80.67\%; 82.15\%; 86.25\%; 87.44\% \\
2012 & \cite{---------} & DCNN and MobileNetV2 & Maize FAW infested leaves, cobs and tassels dataset & --- & 92\% \\
2012 & \cite{---------} & DCNN and YOLOV4 & Google images dataset & --- & 84\% and 95\%\\
2021 & \cite{------------} & DCNN, ANN, SVM, and K-NN & Paddy diseases dataset & --- & 94.08\% \\
2012 & \cite{------------} & VGG16, SVM, K-Means, K-means with SVM, Decision tree & Rice disease dataset & --- & 97\% \\
2012 & \cite{-------} & DCNN, Inception V3, ZF-Net, TextCNN and bidirectional gated recurrent unit (BiGRU); support vector machine (SVM), and extreme learning machine (ELM) models & Rice leaves dataset & --- & 98.58\% \\
2012 & \cite{----------} & DCCN, i.e., Custom-Net, Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 & Pearl millet infected with blast and rust dataset & 0.79; 5.44; 2.19; 23.80; 20.09; 14.78 & 84\% and 98.15\% \\
2012 & \cite{----------} & DCNN, ANN, SVM, and K-NN & PlantVillage & --- & 98.42\% \\
2012 & \cite{-----------} & AlexNet, SqueezeNet, GoogLeNet, ResNet-50, and ResNet-101 & Pearl millet infected with blast and Guava disease dataset & --- & 96.01\% \\
2012 & \cite{-----------} & AlexNet, GoogleNet, VGG16, and VGG19 & Healthy and infected maize dataset & --- & 99.94\% \\
2012 & \cite{-----------} & DCNN, SVM, Decision tree, Linear Regression, K-NN, AlexNet, ResNet, VGG16, InceptionV3 & Alternaria Leaf Spot (ALS) disease dataset & --- & 98.41\% \\
2012 & \cite{---------} & YOLO-V5, VGG-16, AlexNet, ResNet-50, ResNet-101, DenseNet-121 and Bidirectional Cross-Modal Transformer (BiCMT) & Xiaotangshn National Precision Agriculture Demonstration Base dataset & --- & 96.92\% \\
2012 & \cite{--------} & YEYEYEYE, VGG16, VGG19, MobileNetV2, DHDHDHHDD, ResNet-V2, NasNetMobile, Inception-V3 and InceptionResNetV2 & Tipburn Disorder Detection in Strawberry Leaves dataset & 1.008; ;134.27; 139.578; 0.23; 7.77; 21.81; 58.34; 54.34; 4.27 & 96.03\% \\
\bottomrule
\end{tblr}
\end{table}
\end{document}
答案1
欢迎来到 TeX.SE！
尝试使单元格中文本较短的列变窄，而文本较多的列变宽。使用tabularray包可以轻松做到这一点：
\documentclass{article}
\usepackage[margin=25mm]{geometry}
%---------------- show page layout. don't use in a real document!
\usepackage{showframe}
\renewcommand\ShowFrameLinethickness{0.15pt}
\renewcommand*\ShowFrameColor{\color{red}}
%---------------------------------------------------------------%
\usepackage{graphicx}
\usepackage{tabularray}
\UseTblrLibrary{booktabs}
\begin{document}
\begin{table}
\caption{My caption}
\label{my-label}
\footnotesize\sffamily
\begin{tblr}{colsep= 4pt,
colspec={@{}r c X[2, l] X[1.5,l] X[l] X[l] @{}},
row{1} = {font=\bfseries, c, m},
rowsep=3pt
}
\toprule
Yea & SL No. & DL model & Dataset & {No. of\\ parameter (M)} & {Accuracy (Avg)} \\
\midrule
1000 & \cite{1} & Matrix-based CNN; AlexNet and VGG-16 & Winter wheat leaf diseases images & --- & 93.1\% \\
2000 & \cite{2} & Lightweight DCNN and Mask-RCNN & Greenhouse live TJ-Tomato and PlantVillage & --- & 93.61\% \\
3000 & \cite{3} & DCNN & PlantVillage & 0.766 & 93.03\% \\
4000 & \cite{4} & ResNet50; InceptionV2; MobileNetV1 & Deep transfer learning system dataset & --- & 91\% \\
5020 & \cite{5} & Concatenated residual networks (ComNet), VGGNet, AlexNet, ResNet, Xception, and MobileNet & PlantVillage and tea diseases dataset & 98.10 & 90.41\% and 86.17\% \\
20000 & \cite{6} & Pre-trained VGG-16, ResNet, GoogLeNet, Inception-v3, LeNet & Paddy field images & 14.81 & 93.13\% \\
1111 & \cite{7} & Hybrid DenseNet and Xception (XDNet); Inception-v3, MobileNet, VGG16, DenseNet201, Xception, VGG-INCEP & Apple leaf diseases & 10.16 & 91.02\% \\
5643 & \cite{8} & DCNN, Inception-V3, ResNet-50, NasNet-Large, DenseNet-121 & Hydroponic experiments & 23.9; 26.7; 84.4; 8.1 & 41.67\%; 95.15\%; 76.25\%; 27.44\% \\
8356 & \cite{9} & DCNN and MobileNetV2 & Maize FAW infested leaves, cobs and tassels dataset & --- & 97\% \\
8989 & \cite{10} & DCNN and YOLOV4 & Google images dataset & --- & 94\% and 45\% \\
4312 & \cite{11} & DCNN, ANN, SVM, and K-NN & Paddy diseases dataset & --- & 94.08\% \\
7865 & \cite{12} & VGG16, SVM, K-Means, K-means with SVM, Decision tree & Rice disease dataset & --- & 92\% \\
7878 & \cite{13} & DCNN, Inception V3, ZF-Net, TextCNN and bidirectional gated recurrent unit (BiGRU); support vector machine (SVM), and extreme learning machine (ELM) models & Rice leaves dataset & --- & 92.58\% \\
4321 & \cite{14} & DCCN, i.e., Custom-Net, Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 & Pearl millet infected with blast and rust dataset & 0.79; 5.44; 2.19; 23.80; 20.09; 14.78 & 84\% and 98.15\% \\
4444 & \cite{15} & DCNN, ANN, SVM, and K-NN & PlantVillage & --- & 91.42\% \\
6543 & \cite{16} & AlexNet, SqueezeNet, GoogLeNet, ResNet-50, and ResNet-101 & Pearl millet infected with blast and Guava disease dataset & --- & 96.74\% \\
3456 & \cite{17} & AlexNet, GoogleNet, VGG16, and VGG19 & Healthy and infected maize dataset & --- & 96.94\% \\
3432 & \cite{18} & DCNN, SVM, Decision tree, Linear Regression, K-NN, AlexNet, ResNet, VGG16, InceptionV3 & Alternaria Leaf Spot (ALS) disease dataset & --- & 98.41\% \\
9743 & \cite{19} & YOLO-V5, VGG-16, AlexNet, ResNet-50, ResNet-101, DenseNet-121 and Bidirectional Cross-Modal Transformer (BiCMT) & Xiaotangshn National Precision Agriculture Demonstration Base dataset & --- & 96.92\% \\
3432 & \cite{19} & PSO-CNN, VGG16, VGG19, MobileNetV2, EfficientNet, ResNet-V2, NasNetMobile, Inception-V3 and InceptionResNetV2 & Tipburn Disorder Detection in Strawberry Leaves dataset & 1.008; ;134.27; 139.578; 0.23; 7.77; 21.81; 58.34; 54.34; 6.27 & 67.73\% \\
\bottomrule
\end{tblr}
\end{table}
\end{document}
在 Overleaf 中获得的编译结果：
（红线表示页面布局）
附录：
转换为长表：
\documentclass{article}
\usepackage[margin=25mm]{geometry}
%---------------- show page layout. don't use in a real document!
\usepackage{showframe}
\renewcommand\ShowFrameLinethickness{0.15pt}
\renewcommand*\ShowFrameColor{\color{red}}
%---------------------------------------------------------------%
\usepackage{graphicx}
\usepackage{tabularray}
\UseTblrLibrary{booktabs}
\begin{document}
\begingroup
\small\sffamily\linespread{0.92}\selectfont
\begin{longtblr}[
caption = {My caption},
label = {my-label}
]{rowhead = 1,
colsep= 4pt,
colspec={@{}r c X[2, l] X[1.5,l] X[l] X[l] @{}},
row{1} = {font=\bfseries, c, m},
rowsep=2pt
}
\toprule
Yea &{SL\\ No.}& {DL\\ model} & Dataset & {No. of\\ parameter (M)} & {Accuracy\\ (Avg)} \\
\midrule
%
% table body is the same as before
%
