{rfName}
In

Indexed in

License and use

Icono OpenAccess

Citations

136

Altmetrics

Analysis of institutional authors

Sancho-Knapik DAuthorPeguero-Pina JjAuthorGil-Pelegrín EAuthor

Share

November 13, 2019
Publications
>
Article
Sí

Instantaneous and non-destructive relative water content estimation from deep learning applied to resonant ultrasonic spectra of plant leaves

Publicated to:Plant Methods. 15 (1): 128-128 - 2019-11-07 15(1), DOI: 10.1186/s13007-019-0511-z

Authors: Dolores Farinas, Maria; Jimenez-Carretero, Daniel; Sancho-Knapik, Domingo; Javier Peguero-Pina, Jose; Gil-Pelegrin, Eustaquio; Gomez Alvarez-Arenas, Tomas

Affiliations

1Department of Food Technology, Universitat Politècnica de València (UPV), Valencia, Spain. - Author
2Cellomics Unit, Cell & Developmental Biology Area, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain. - Author
3Unidad de Recursos Naturales, Centro de Investigación y Tecnología Agroalimentaria Gobierno de Aragón (CITA), Zaragoza, Spain. - Author
4Sensors and Ultrasonic Technologies Department, Information and Physics Technologies Institute, Spanish National Research Council (CSIC), Madrid, Spain. - Author
CNIC, Cell Unit, Cell & Dev Biol Area, Madrid, Spain - Author
CSIC, Sensors & Ultrason Technol Dept, Informat & Phys Technol Inst, Madrid, Spain - Author
Ctr Invest & Tecnol Agroalimentaria Gobiemo Arago, Unidad Recursos Nat, Zaragoza, Spain - Author
Univ Politecn Valencia, Dept Food Technol, Valencia, Spain - Author
See more

Abstract

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.© The Author(s) 2019.

Keywords

IrrigationMachine learningNc-rusPlant leavesRwcUltrasounds

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Plant Methods due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2019, it was in position 36/234, thus managing to position itself as a Q1 (Primer Cuartil), in the category Plant Sciences.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.07. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.26 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 5.74 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-13, the following number of citations:

  • WoS: 43
  • Scopus: 43
  • Europe PMC: 9
  • Google Scholar: 40

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-13:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 87.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 94 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 3.1.
  • The number of mentions on the social network X (formerly Twitter): 6 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.