Instantaneous and non-destructive relative water content estimation from deep learning applied to resonant ultrasonic spectra of plant leaves
Instantaneous and non-destructive relative water content estimation from deep learning applied to resonant ultrasonic spectra of plant leaves
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DOI：
10.1186/s13007-019-0511-z
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发表时间：
2019-11-07
期刊：
影响因子：
5.1
通讯作者：
Gomez Alvarez-Arenas, Tomas
中科院分区：
文献类型：
--
作者：
Dolores Farinas, Maria;Jimenez-Carretero, Daniel;Gomez Alvarez-Arenas, Tomas
背景非接触共振超声波谱(NC-RUS)已被证明是一种可靠的动态测定叶片水分状态的技术。它已经在50多种植物上进行了测试。同时，相对含水率(RWC)在生态生理领域也被广泛用于描述植物叶片的水分饱和程度。获得RWC意味着一个繁琐和破坏性的过程，可能会带来人工制品，而且不能立即确定。结果提出了一种利用非接触共振超声(NC-RUS)数据估算植物叶片相对含水量的方法。该技术能够以非侵入性、非破坏性和快速的方式收集[0.15-1.6]MHz频率范围内的植物叶片的传输系数。对两种不同的方法进行了评估：卷积神经网络(CNN)和随机森林(RF)。CNN采用从叶片获取的全部超声波频谱，而RF仅使用由传输系数数据产生的四个相关参数。这两种方法都成功地在Viburnum tinus叶片样品中进行了测试，皮尔逊相关系数在0.92到0.84之间。结论NC-RUS技术与深度学习算法相结合，是一种实时、准确、无损测定植物叶片RWC的可靠工具。
Background 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. Results 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. Conclusions 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.