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arXiv · 2310.04056

Physics-assisted machine learning for THz spectroscopy: sensing moisture on plant leaves

Milan Koumans·Daan Meulendijks·Haiko Middeljans·Djero Peeters·Jacob C. Douma·Dook van Mechelen
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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 a moist plant leaf for 12,000 distinct water patterns 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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Milan Koumans, Daan Meulendijks, Haiko Middeljans, Djero Peeters, Jacob C. Douma, Dook van Mechelen. 2023-10-06. Physics-assisted machine learning for THz spectroscopy: sensing moisture on plant leaves. https://arxiv.org/abs/2310.04056

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