Presentation Information
[11a-PA3-14]Investigation of electrochemical impedance spectroscopy (EIS) characteristics for estimating plant transpiration using machine learning
〇Yosuke Umemoto1, Mutsumi Sugiyama1,2 (1.Tokyo Univ. Sci., 2.RIST)
Keywords:
plant,transpiration,electrochemical impedance property
In this study, we evaluated the relationship between EIS characteristics observed in petioles and transpiration rates. We also used EIS data to estimate and analyze transpiration rates using machine learning, and examined the model’s prediction accuracy and the importance of each explanatory variable.