I have a table from the MDPI journal template but it is too big and I need to break it down to the remaining pages. However, the template does not have this information.
Having said that I need help in order to maintain the content and formatting of the table while it breaks down to the other pages.
I have a table from the MDPI magazine template only it is too big and I need it broken down to the remaining sheets. However, the template does not have this information.
Having said that I need help in order to maintain the content and formatting of the table while it breaks down to the other pages.
I've tested several things I've found in my research but it's not working because of the table's formatting specifics.
The code that puts the table in the form I want and that is the MDPI journal form is as follows:
\begin{table}[H] \caption{Data-set description of papers analyzed..\label{Table:Datasets_Description}} \begin{adjustwidth}{-\extralength}{0cm} \newcolumntype{C}{>{\centering\arraybackslash}X} \begin{tabularx}{\fulllength}{CCCCCCCCCC} \toprule \textbf{Crop} & \textbf{Disease/Pest} & \textbf{Context} & \textbf{No. of data} & \textbf{Parameters (raw data only)} & \textbf{Location} & \textbf{Date} & \textbf{Availability} & \textbf{Study} \\ \midrule - & General insects & Insect counting with computer vision and weather relationships & 2 periods of 5 days & Temp., pressure, humidity, light intensity, insect count & Taiwan & 2016, 2017 & - & \cite{R_T_2017} \\ \midrule 14 crops & 26 diseases & Disease classification & 54306 & - & - & 2016 & - & \cite{M_2016} \\ \midrule Cotton & 10 pests and diseases & Weather based models for disease prediction & 15343 weekly records & $Temp_{max}$ , $Temp_{min}$, RH morning, RH evening, Rainfall, Wind Speed, Sunshine Hour, Evaporation & 6 regions in india & - & Publicly \cite{CPDWD} & \cite{X_2019} \\ \midrule Papaya & Unnamed disease & Disease classification using computer vision & 160 & - & - & 2018 & - & \cite{M_2018} \\ \midrule Pomegranate & Unnamed disease & Leaf disease grading & - & - & India & 2010 & - & \cite{S_2011} \\ \midrule Potato & Late blight & Near InfraRed pictures taken by drone & 504 & - & Colombia & 2018 & - & \cite{D_2018} \\ \midrule Rice & Rice blast & Weather based models for disease prediction & Weekly records & $Temp_{max}$ , $Temp_{min}$, $RH_{max}$ , $RH_{min}$, Rainfall, Rain Days/week & 5 regions in India & 2000 - 2004 & - & \cite{K_2006} \\ \midrule Rice & Brown planthopper & Weather based models for pest prediction & Monthly averages of parameters & Pest catches, $Temp_{max}$ , $Temp_{min}$, RH, and rainfall, NDVI & 4 regions in Thailand & 2006-2016 & - & \cite{S_2019} \\ \midrule Sugarcane & Brown spot & Leaf disease grading & 90 & - & - & 2011 & - & \cite{P_2011} \\ \midrule Tomato & Leafminer infection & Analysis of leaf reflectance for different infection degrees & 300 & - & China & 2006 & - & \cite{D_2006} \\ \midrule Tomato & Tomato Powdery Mildew Fungus & Disease classification using thermal and stereo visible light images & 71 plants * 14 days & - & University of Warwick, UK & 2015 & - & \cite{R_2015} \\ \midrule Tomato & Tomato Yellow Leaf Curl (TYLCV) and the Tomato Spotted Wilt Virus (TSWV) & Disease detection using computer vision & 100 for each virus & - & - & 2015 & - & \cite{M_2015} \\ \midrule Pear, peach, apple, pair, grapevine & 13 diseases (+1 class for healthy leaves +1 class for background) & Disease classification & 3000 & - & - & 2016 & - & \cite{S_2016} \\ \midrule Apple orchards & Coddling moths & Pest classification on traps (coddling moths vs general insects) & ~300 & - & Italy & 2020 & - & \cite{A_2020} \\ \midrule 8 (rice, corn, wheat, bet, alfalfa, vitis, citrus, mango) & 102 pests & Pest classification in big data-set with various stages of insect development & 75000 & - & - & 2019 & yes & \cite{W_2019} \\ \midrule tomato & 2 types of whiteflies & 2 types of similar pest detection, at insect and egg level & 4331 & - & Czech Republic & -2019 & Upon request & \cite{G_2019} \\ \midrule tomato & Tomato Yellow Leaf Curl Virus, Tomato mosaic virus , Target Spot , Spider mites, Septoria spot, Leaf Mold, Lateblight, Earlyblight, Bacterial Spot & Disease detection & 14828 & - & - & 2017 & yes & \cite{B_2017} \\ \midrule 25 crops & 58 classes for different disease and healthy plants & Disease classification in field vs lab conditions & 87,848 & - & - & 2018 & - & \cite{A_2018} \\ \midrule 13 crops & 17 diseases & Disease classification in field vs lab conditions & 2598 & - & - & 2020 & yes & \cite{S_2020} \\ \midrule Banana & Healthy, black sigatoka, black speckle & Disease classification & 3700 & - & - & 2017 & yes & \cite{A_2017} \\ \midrule Cucumber and Pumpkin species & Powdery mildew disease & Disease detection & Seven-week-old & - & - & 2011 & - & \cite{SN_2011} \\ \midrule Rice & No infestation, mild infestation, moderate infestation,severe infestation & Stress detection & 20 containers & - & China & 2010 & - & \cite{Z_2013} \\ \midrule Corn & Exserohilum Turcicum, Bipolaris Maydis, Cercospora zeaemaydis Tehon and Daniels, Curvularia Lunata Boed, Puccinia Polysora, Physoderma Maydis Miyabe & Disease detection & 300 & - & - & 2011 & - & \cite{P_J_2011} \\ \midrule Tomato & Tomato Yellow Leaf Curl Virus, Tomato mosaic virus , Target Spot , Spider mites, Septoria spot, Leaf Mold, Lateblight, Early blight, Bacterial Spot & Disease Classification & 16268 & - & - & 2021 & Yes & \cite{T_2021} \\ \bottomrule \end{tabularx} \end{adjustwidth} \end{table} And the result of this PDF code is as follows:
longtablehad not to be enclosed in floattable. It prevent it to break into more pages. Another possibilities is that you split table in two parts manually and than consider idea described in tex.stackexchange.com/questions/278727/…