SapFlower: An automated tool for sap flow data preprocessing, gap filling, and analysis using deep learning

SapFlower is an interactive and deep learning powered tool for processing sap flux data from thermal dissipation probes (TDP; Granier 1985, 1987). It is designed for (1) automated data cleaning, (2) state-of-the-art machine learning modeling for gap-filling, and (3) estimating of sap flux from raw TDP data. SapFlower integrates some important features from Baseliner 4, developed by by A. Christopher Oishi & David Hawthorne.

Please note that some functions, such as data smoothing (for some cases), haven't been enabled.

Here Project_Pontotoc_2020.html and Simplified_sapflow.csv are provided an example for users to explore the functions of SapFlower. Users just need download, install SapFlower and MATLAB Runtime, and run SapFlower. User can click the Open Project button in Projection Configuration Tab, define the DataPath to create and load project. 

SapFlower.mlappinstall is for those who want to run the GUI app in their MATLAB.

SapFlower_Installer_web.app.zip is for those who want to use it as a standalone app on macOS. 

SapFlower_Installer_web.exe can be installed as a standalone app on Windows. The MATLAB Runtime could not be successfully added to your system path. You must add the following folder manually: 
C:\Program Files\MATLAB\MATLAB Runtime\R2024a\runtime\win64  to ensure you can run SapFlower.

If you have problem to download MATLAB Runtime while installing SapFlowr,  you can download SapFlower_mcr.zip, which includes MATLAB Runtime.

Simplified_sapflow.csv and Project_Pontotoc_2020.html are toy data and project for users to test or treat them as examples.