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

TL;DR

This paper demonstrates how physics-informed machine learning models, including decision trees and CNNs, can effectively analyze THz spectroscopy data to determine leaf wetness, aiding plant disease prediction.

Contribution

It introduces a physics-assisted machine learning approach for THz spectroscopy data to assess leaf wetness, with insights on model generalizability to real-world variations.

Findings

Decision trees and CNNs can accurately classify leaf wetness levels.

Physics-motivated feature selection improves model performance.

Models show promising generalizability to unseen water patterns.

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.