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

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However, sap flow data frequently include noise, outliers, and gaps, creating challenges for analysis and requiring substantial manual processing. We developed SapFlower, a tool that automates data preprocessing, model training, gap-filling, sapwood area scaling and modeling, and water use analysis. It integrates autocleaning, machine learning and deep learning models (e.g. random forest, Gaussian process regression, long short-term memory (LSTM), bidirectional LSTM (BiLSTM)), and efficient workflows to process sap flow data. SapFlower can remove over 90% of noisy data while preserving legitimate variations and achieve high accuracy in gap-filling based on user-determined parameters. Random forest, LSTM, and BiLSTM models reduced root mean square error to 10% or less for long-term gaps. Model training and prediction can be performed efficiently within seconds. SapFlower significantly enhances the efficiency and accessibility of TDP data analysis by automating complex tasks, enabling researchers without programming expertise to employ advanced techniques. Future improvements will focus on species-specific corrections for TDP and support for additional measurement methods. SapFlower is openly available on GitHub (https://github.com/JiaxinWang123/SapFlower) and Zenodo (doi: 10.5281/zenodo.13665919).", "claims": [{"id": "claim-method", "statement": "During model training for gap‐filling, we fine‐tuned hyperparameters to achieve an RMSE of c. 10% or less of the average raw sap flow measurements (e.g. in ΔV, millivolts).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40200603", "locator": "sections[28] \"Model training and gap‐filling\" paragraph 2", "quote": "During model training for gap‐filling, we fine‐tuned hyperparameters to achieve an RMSE of c. 10% or less of the average raw sap flow measurements (e.g. in ΔV, millivolts)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are openly available in Zenodo at doi: 10.5281/zenodo.13665919.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40200603", "locator": "sections[43] \"Data availability\" paragraph 2", "quote": "The data that support the findings of this study are openly available in Zenodo at doi: 10.5281/zenodo.13665919."}], "state": "complete"}, {"id": "claim-target-result", "statement": "SapFlower removed over 90% of noisy data and reduced root mean square error to 10% or less for long-term gaps using random forest, LSTM, and BiLSTM models.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40200603", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "SapFlower can remove over 90% of noisy data while preserving legitimate variations and achieve high accuracy in gap‐filling based on user‐determined parameters. 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