ArticleScientific reports2024
Physics-assisted machine learning for THz time-domain spectroscopy: sensing leaf wetness.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 8 citations in OpenAlex.
- Coffee Bean Characterization Using Terahertz Sensing.Sensors (Basel, Switzerland) · 2025Article
- Terahertz Fingerprint Metasurface Sensor Based on Temperature Variation for Trace Molecules.Biosensors · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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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.
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