Evidence map›Paper›PMID 38005747›Full record

ArticlePlants (Basel, Switzerland)2023

Field Plant Monitoring from Macro to Micro Scale: Feasibility and Validation of Combined Field Monitoring Approaches from Remote to in Vivo to Cope with Drought Stress in Tomato.

Filippo Vurro, Michele Croci, Giorgio Impollonia, Edoardo Marchetti, Adrian Gracia-Romero, Manuele Bettelli, José Luis Araus, Stefano Amaducci, Michela Janni

Open access · goldAbstract read
In one paragraph

Article in Plants (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 2 countries.

Filippo VurroIstituto dei Materiali per l'Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy.
Michele CrociDepartment of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy.ORCID 0000-0001-7356-2774
Giorgio ImpolloniaDepartment of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy.ORCID 0000-0002-9878-6595
Edoardo MarchettiIstituto dei Materiali per l'Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy.
Adrian Gracia-RomeroIntegrative Crop Ecophysiology Group, Agrotecnio-Center for Research in Agrotechnology, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain.ORCID 0000-0001-8308-9693
Manuele BettelliIstituto dei Materiali per l'Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy.ORCID 0000-0003-4062-3782
José Luis ArausIntegrative Crop Ecophysiology Group, Agrotecnio-Center for Research in Agrotechnology, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain.ORCID 0000-0002-8866-2388
Stefano AmaducciDepartment of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy.
Michela JanniIstituto dei Materiali per l'Elettronica e il Magnetismo (IMEM-CNR), Parco Area delle Scienze 37/A, 43124 Parma, Italy.ORCID 0000-0002-3414-1122
Institute of Materials for Electronics and Magnetism · ITUniversità Cattolica del Sacro Cuore · ITInstitute of Agrifood Research and Technology · ESUniversitat de Barcelona · ES

Funding

Emilia Romagna Valorizzazione Economica Territorio (Italy) Regione Emilia Romagna ERDF project 2014-2020
6 · The paper itself

Abstract

Monitoring plant growth and development during cultivation to optimize resource use efficiency is crucial to achieve an increased sustainability of agriculture systems and ensure food security. In this study, we compared field monitoring approaches from the macro to micro scale with the aim of developing novel in vivo tools for field phenotyping and advancing the efficiency of drought stress detection at the field level. To this end, we tested different methodologies in the monitoring of tomato growth under different water regimes: (i) micro-scale (inserted in the plant stem) real-time monitoring with an organic electrochemical transistor (OECT)-based sensor, namely a bioristor, that enables continuous monitoring of the plant; (ii) medium-scale (<1 m from the canopy) monitoring through red-green-blue (RGB) low-cost imaging; (iii) macro-scale multispectral and thermal monitoring using an unmanned aerial vehicle (UAV). High correlations between aerial and proximal remote sensing were found with chlorophyll-related indices, although at specific time points (NDVI and NDRE with GGA and SPAD). The ion concentration and allocation monitored by the index R of the bioristor during the drought defense response were highly correlated with the water use indices (Crop Water Stress Index (CSWI), relative water content (RWC), vapor pressure deficit (VPD)). A high negative correlation was observed with the CWSI and, in turn, with the RWC. Although proximal remote sensing measurements correlated well with water stress indices, vegetation indices provide information about the crop's status at a specific moment. Meanwhile, the bioristor continuously monitors the ion movements and the correlated water use during plant growth and development, making this tool a promising device for field monitoring.

Indexed as

bioristorfield monitoringmultispectralphenotypingprecision agricultureRGB-based indexsensorstomatoUAVvegetation indices

Identifiers

PMID38005747
PMCPMC10674827
OpenAlexW4388671831

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.