Instantaneous and non-destructive relative water content estimation from deep learning applied to resonant ultrasonic spectra of plant leaves
| Autor: | Tomas Gomez Alvarez-Arenas, Daniel Jimenez-Carretero, Domingo Sancho-Knapik, M.D. Fariñas, José Javier Peguero-Pina, Eustaquio Gil-Pelegrín |
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| Přispěvatelé: | European Commission, CSIC - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Ministerio de Economía y Competitividad (España), Gobierno de Aragón, Generalitat de Catalunya, Ministerio de Ciencia, Innovación y Universidades (España), Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (España), Gobierno de Aragón (España) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
02 engineering and technology Plant Science lcsh:Plant culture 01 natural sciences Convolutional neural network RWC Ultrasounds Machine learning NC-RUS Genetics Range (statistics) lcsh:SB1-1110 Transmission coefficient Water content lcsh:QH301-705.5 Irrigation Mathematics Resonant ultrasound spectroscopy biology Research Relaciones planta agua 021001 nanoscience & nanotechnology biology.organism_classification Random forest lcsh:Biology (General) Hojas Tratamiento con ultrasonido Contenido de agua Viburnum tinus Ultrasonic sensor 0210 nano-technology Biological system 010606 plant biology & botany Biotechnology Plant leaves |
| Zdroj: | Plant Methods, Vol 15, Iss 1, Pp 1-10 (2019) Zaguán. Repositorio Digital de la Universidad de Zaragoza instname Plant Methods Digital.CSIC. Repositorio Institucional del CSIC Zaguán: Repositorio Digital de la Universidad de Zaragoza Universidad de Zaragoza Repisalud Instituto de Salud Carlos III (ISCIII) |
| Popis: | [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. This research was supported by Grant (DPI2016-78876-R-AEI/FEDER, UE) from the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERDF/FEDER). It was also partially funded by Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Grant number RTA2015-00054-C02-01 and by Gobierno de Aragón H09_17R research group (DOC INIA-CCAA (ESF)). Funding was provided by Conselleria d’Educació, Investigació, Cultura i Esport (APOSTD/2018/203 (ESF)). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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