Transpiration Insights: Estimating Transpiration Through Advanced Modeling

Abstract

O_LIWe 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. C_LIO_LIData 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. C_LIO_LIThe Random Forest and XGBoost models demonstrated high accuracy in predicting the whole plant transpiration, with R{superscript 2} 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. C_LIO_LIOur 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. C_LI

Plant phenotyping relevance

機械学習モデルにより植物個体の蒸散量を推定する手法を開発・検証しており、植物生理形質の取得・推定が研究の中心である。

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