Physics-assisted machine learning for THz time-domain spectroscopy: sensing leaf wetness

Milan Koumans, Daan Meulendijks, Haiko Middeljans, Djero Peeters, Jacob C. Douma, Dook van Mechelen*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Signal processing techniques are of vital importance to bring THz spectroscopy to a maturity level to reach practical applications. In this work, we illustrate the use of machine learning techniques for THz time-domain spectroscopy assisted by domain knowledge based on light–matter interactions. We aim at the potential agriculture application to determine the amount of free water on plant leaves, so-called leaf wetness. This quantity is important for understanding and predicting plant diseases that need leaf wetness for disease development. The overall transmission of 12,000 distinct water droplet patterns on a plastized leaf was experimentally acquired using THz time-domain spectroscopy. We report on key insights of applying decision trees and convolutional neural networks to the data using physics-motivated choices. Eventually, we discuss the generalizability of these models to determine leaf wetness after testing them on cases with increasing deviations from the training set.

Original languageEnglish
Article number7034
JournalScientific Reports
Volume14
Issue number1
DOIs
Publication statusPublished - 25 Mar 2024

Fingerprint

Dive into the research topics of 'Physics-assisted machine learning for THz time-domain spectroscopy: sensing leaf wetness'. Together they form a unique fingerprint.

Cite this