Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands.
Centre for Crop System Analysis, Wageningen University, 6700 AK, Wageningen, The Netherlands.
Sci Rep. 2024 Mar 25;14(1):7034. doi: 10.1038/s41598-024-57161-4.
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.
信号处理技术对于太赫兹光谱技术达到实用水平至关重要。在这项工作中，我们说明了基于光物质相互作用的基于领域知识的机器学习技术在太赫兹时域光谱学中的应用。我们的目标是在农业方面的潜在应用，以确定植物叶片上自由水的量，即叶片湿润度。对于了解和预测需要叶片湿润度才能发展的植物病害，这种数量非常重要。使用太赫兹时域光谱技术，实验采集了 12000 个不同的水滴滴落模式在塑化叶片上的整体透射率。我们报告了应用决策树和卷积神经网络的关键见解，这些见解是基于物理启发的选择来处理数据的。最后，我们讨论了这些模型的泛化能力，即在测试了与训练集偏差越来越大的案例后，用于确定叶片湿润度。
