Evidence map›Paper›PMID 37631715›Full record

ArticleSensors (Basel, Switzerland)2023

Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture-A Case Study.

Alexander Erler, Daniel Riebe, Toralf Beitz, Hans-Gerd Löhmannsröben, Mathias Leenen, Stefan Pätzold, Markus Ostermann, Michal Wójcik

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

8 authors.

Alexander ErlerPhysical Chemistry, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany.ORCID 0000-0001-5732-3685
Daniel RiebePhysical Chemistry, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany.ORCID 0000-0002-1234-6733
Toralf BeitzPhysical Chemistry, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany.
Hans-Gerd LöhmannsröbenPhysical Chemistry, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany.
Mathias LeenenSoil Science and Soil Ecology, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Nussallee 13, 53115 Bonn, Germany.
Stefan PätzoldSoil Science and Soil Ecology, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Nussallee 13, 53115 Bonn, Germany.ORCID 0000-0002-9739-8734
Markus OstermannProcess Analytical Technology, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany.
Michal WójcikDepartment of Field Theory, Electronic Circuits and Optoelectronics, Faculty of Electronics, Photonics and Microsystems, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50370 Wroclaw, Poland.ORCID 0000-0001-7888-4019

Funding

Federal Ministry of Education and Research 031B0513H
6 · The paper itself

Abstract

In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer. The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements.

Indexed as

feature selectionLIBSmultivariate methodsprecision agriculturesoil

Identifiers

PMID37631715
PMCPMC10459606

What OpenQuestion holds

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Registered trials

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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.