Physics-assisted machine learning for THz spectroscopy: sensing moisture on plant leaves
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.
Keywords
Cite
@article{arxiv.2310.04056,
title = {Physics-assisted machine learning for THz spectroscopy: sensing moisture on plant leaves},
author = {Milan Koumans and Daan Meulendijks and Haiko Middeljans and Djero Peeters and Jacob C. Douma and Dook van Mechelen},
journal= {arXiv preprint arXiv:2310.04056},
year = {2023}
}
Comments
11 pages, 6 figures