Evidence map›Paper›PMID 38400299›Full record

ArticleSensors (Basel, Switzerland)2024

Low-Cost Optical Sensors for Soil Composition Monitoring.

Francisco Javier Diaz, Ali Ahmad, Lorena Parra, Sandra Sendra, Jaime Lloret

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Francisco Javier DiazInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Gandía C/Paranimf, 1, 46730 Grao de Gandia, Spain.ORCID 0009-0009-1458-7547
Ali AhmadInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Gandía C/Paranimf, 1, 46730 Grao de Gandia, Spain.ORCID 0000-0001-5530-7374
Lorena ParraInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Gandía C/Paranimf, 1, 46730 Grao de Gandia, Spain.ORCID 0000-0001-9215-8734
Sandra SendraInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Gandía C/Paranimf, 1, 46730 Grao de Gandia, Spain.ORCID 0000-0001-9556-9088
Jaime LloretInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Gandía C/Paranimf, 1, 46730 Grao de Gandia, Spain.ORCID 0000-0002-0862-0533

Funding

Generalitat Valenciana THINKINAZUL/2021/002Ministerio de Ciencia e Innovación PID2020-114467RR-C33/AEI/10.13039/501100011033Ministerio de Ciencia e Innovación TED2021-131040B-C31
6 · The paper itself

Abstract

Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time and resource consumption. Contrastingly, sensor techniques effectively gather environmental data, though they mostly lack in situ studies. Despite this, sensors offer substantial on-site data generation potential in a non-invasive manner and can be included in wireless sensor networks. Therefore, the aim of the paper is to develop a low-cost red, green, and blue (RGB)-based sensor system capable of detecting changes in the composition of the soil. The proposed sensor system was found to be effective when the sample materials, including salt, sand, and nitro phosphate, were determined under eight different RGB lights. Statistical analyses showed that each material could be classified with significant differences based on specific light variations. The results from a discriminant analysis documented the 100% prediction accuracy of the system. In order to use the minimum number of colors, all the possible color combinations were evaluated. Consequently, a combination of six colors for salt and nitro phosphate successfully classified the materials, whereas all the eight colors were found to be effective for classifying sand samples. The proposed low-cost RGB sensor system provides an economically viable and easily accessible solution for soil classification.

Indexed as

agricultural practicesdryland agricultureoptical sensorsalinitysoil fertilizersoil propertiesWSN

Identifiers

PMID38400299
PMCPMC10892096

What OpenQuestion holds

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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.