用于太赫兹光谱的物理辅助机器学习：植物叶片湿度传感
Physics-assisted machine learning for THz spectroscopy: sensing moisture on plant leaves
AI总结：
本研究面向农业叶片湿度检测需求，提出结合光与物质相互作用领域知识的物理辅助机器学习方法，基于12000种水分模式的太赫兹时域光谱数据，验证了决策树与卷积神经网络的效果，并探讨了模型泛化性。
摘要：
信号处理技术对推动太赫兹光谱技术走向成熟、实现实际应用至关重要。本研究展示了在基于光与物质相互作用的领域知识辅助下，将机器学习技术应用于太赫兹时域光谱的方法。研究面向农业应用场景，目标是测定植物叶片上的自由水含量，即叶片湿度。该指标对于理解和预测需要叶片湿度条件才能发展的植物病害具有重要意义。研究通过太赫兹时域光谱，实验采集了12000种不同水分分布模式下湿润植物叶片的总透射率数据。报告了基于物理动机的选择，将决策树和卷积神经网络应用于该数据的关键见解。最后，在与训练集偏差逐渐增大的案例上测试模型后，讨论了这些模型测定叶片湿度的泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
