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)

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Samenvatting

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.

Originele taal-2Engels
Artikelnummer7034
Aantal pagina's11
TijdschriftScientific Reports
Volume14
Nummer van het tijdschrift1
DOI's
StatusGepubliceerd - 25 mrt. 2024

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Publisher Copyright:
© The Author(s) 2024.

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