Integrating Load-Cell Lysimetry and Machine Learning for Prediction of Daily Plant Transpiration

Summary We conducted research to predict daily transpiration in crops by utilizing a combination of machine learning (ML) models combined with extensive transpiration data from gravimetric load cells and ambient sensors. Our aim was to improve the accuracy of transpiration estimates. Data were collected from hundreds of plant specimens growing in two semi-controlled greenhouses over seven years, automatically measuring key physiological traits (serves as our ground truth data) and meteorological variables with high temporal resolution and accuracy. We trained Decision tree, Random Forest, XGBoost, and Neural Network models on this dataset to predict daily transpiration. The Random Forest and XGBoost models demonstrated high accuracy in predicting the whole plant transpiration, with R² values of 0.89 on the test set (cross-validation) and R2 = 0.82 on holdout experiments. Ambient temperature was identified as the most influential environmental factors affecting transpiration. Our results emphasize the potential of ML for precise water management in agriculture, and simplify some of the complex and dynamic environmental forces that shape transpiration. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵† These authors share first authorship We expanded the literature review to include a systematic analysis of recent studies utilizing machine learning for transpiration prediction. Several new figures were added to illustrate the experimental setup, greenhouses, and data architecture. External validation results were incorporated using independently collected datasets from a separate greenhouse and a controlled growth room environment, demonstrating the robustness and generalizability of the model. The greenhouse feature was removed, and the dataset properties, results, and hyperparameter tuning procedures were updated accordingly. Minor text clarifications and structural edits were made throughout to improve readability and coherence.