Non-contact resonant ultrasound spectroscopy (NC-RUS) has been proven as a reliable technique for the dynamic determination of leaf water status. It has been already tested in more than 50 plant species. In parallel, relative water content (RWC) is highly used in the ecophysiological field to describe the degree of water saturation in plant leaves. Obtaining RWC implies a cumbersome and destructive process that can introduce artefacts and cannot be determined instantaneously. Here, we present a method for the estimation of RWC in plant leaves from non-contact resonant ultrasound spectroscopy (NC-RUS) data. This technique enables to collect transmission coefficient in a [0.15–1.6] MHz frequency range from plant leaves in a non-invasive, non-destructive and rapid way. Two different approaches for the proposed method are evaluated: convolutional neural networks (CNN) and random forest (RF). While CNN takes the entire ultrasonic spectra acquired from the leaves, RF only uses four relevant parameters resulted from the transmission coefficient data. Both methods were tested successfully in Viburnum tinus leaf samples with Pearson’s correlations between 0.92 and 0.84. This study showed that the combination of NC-RUS technique with deep learning algorithms is a robust tool for the instantaneous, accurate and non-destructive determination of RWC in plant leaves.

中文翻译：

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基于深度学习的瞬时和非破坏性相对含水量估计应用于植物叶片的共振超声光谱

非接触式共振超声光谱 (NC-RUS) 已被证明是动态测定叶片水分状态的可靠技术。它已经在 50 多种植物中进行了测试。同时，相对水分含量（RWC）在生态生理学领域被广泛用于描述植物叶片的水分饱和程度。获得 RWC 意味着一个繁琐且具有破坏性的过程，该过程可能会引入人工制品并且无法立即确定。在这里，我们提出了一种从非接触共振超声光谱 (NC-RUS) 数据估计植物叶片中 RWC 的方法。该技术能够以非侵入性、非破坏性和快速的方式从植物叶子中收集[0.15-1.6] MHz频率范围内的透射系数。评估了所提出方法的两种不同方法：卷积神经网络（CNN）和随机森林（RF）。虽然 CNN 采用从叶子获取的整个超声波光谱，但 RF 仅使用由传输系数数据得出的四个相关参数。两种方法都在 Viburnum tinus 叶样品中成功测试，Pearson 相关系数在 0.92 和 0.84 之间。本研究表明，将 NC-RUS 技术与深度学习算法相结合是一种强大的工具，可用于瞬时、准确和无损地测定植物叶片中的 RWC。