2026-06-14T03:54:32Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/89872024-09-27T10:13:18Zcom_20.500.12105_19604com_20.500.12105_2051col_20.500.12105_19607

00925njm 22002777a 4500 dc Fariñas, María Dolores author Jimenez-Carretero, Daniel author Sancho-Knapik, Domingo author Peguero-Pina, José Javier author Gil-Pelegrín, Eustaquio author Gómez Álvarez-Arenas, Tomás author 2019-11 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. Plant Methods. 2019; 15:128 10.1186/s13007-019-0511-z 1746-4811 Plant methods 31709000 http://hdl.handle.net/20.500.12105/8987 Irrigation Machine learning NC-RUS Plant leaves RWC Ultrasounds Instantaneous and non-destructive relative water content estimation from deep learning applied to resonant ultrasonic spectra of plant leaves