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

Abstract

ABSTRACT We conducted research to predict daily transpiration in crops by utilising 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 7 years, automatically measuring key physiological traits (serving 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 data set to predict daily transpiration. The Random Forest and XGBoost models demonstrated high accuracy in predicting the whole plant transpiration, with R 2 values of 0.89 on the test set (cross‐validation) and R 2 = 0.82 on holdout experiments. Ambient temperature was identified as the most influential environmental factor affecting transpiration. Our results emphasise the potential of ML for precise water management in agriculture, and simplify some of the complex and dynamic environmental forces that shape transpiration.

Plant phenotyping relevance

植物の個体蒸散量を荷重セル・環境センサーと機械学習で推定し、複数モデルの精度検証を行うことが研究の中心であるため。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.