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-2 | Engels |
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Artikelnummer | 7034 |
Aantal pagina's | 11 |
Tijdschrift | Scientific Reports |
Volume | 14 |
Nummer van het tijdschrift | 1 |
DOI's | |
Status | Gepubliceerd - 25 mrt. 2024 |
Bibliografische nota
Publisher Copyright:© The Author(s) 2024.