Evidence map›Paper›PMID 38778037›Full record

ArticleScientific reports2024

The use of image analysis to study the effect of moisture content on the physical properties of grains.

Łukasz Gierz, Mustafa Ahmed Jalal Al-Sammarraie, Osman Özbek, Piotr Markowski

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. 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

4 authors.

Łukasz GierzInstitute of Machine Design, Faculty of Mechanical Engineering, Poznan University of Technology, ul. Piotrowo 3, 60-965, Poznan, Poland. lukasz.gierz@put.poznan.pl.
Mustafa Ahmed Jalal Al-SammarraieDepartment of Agricultural Machinery and Equipment, College of Agricultural Engineering Sciences, University of Baghdad, Baghdad, Iraq.
Osman ÖzbekDepartment of Agricultural Machineries and Technologies Engineering, Faculty of Agriculture, Selçuk University, 42250, Konya, Turkey.
Piotr MarkowskiDepartment of Heavy-Duty Machines and Research Methodology, University of Warmia and Mazury, ul. Oczapowskiego 11, 10-719, Olsztyn, Poland.

Funding

Narodowe Centrum Badań i Rozwoju LIDER/24/0137/L-8/16/NCBIR/2017
6 · The paper itself

Abstract

Designing machines and equipment for post-harvest operations of agricultural products requires information about their physical properties. The aim of the work was to evaluate the possibility of introducing a new approach to predict the moisture content in bean and corn seeds based on measuring their dimensions using image analysis using artificial neural networks (ANN). Experimental tests were carried out at three levels of wet basis moisture content of seeds: 9, 13 and 17%. The analysis of the results showed a direct relationship between the wet basis moisture content and the main dimensions of the seeds. Based on the statistical analysis of the seed material, it was shown that the characteristics examined have a normal or close to normal distribution, and the seed material used in the investigation is representative. Furthermore, the use of artificial neural networks to predict the wet basis moisture content of seeds based on changes in their dimensions has an efficiency of 82%. The results obtained from the method used in this work are very promising for predicting the moisture content.

Indexed as

Neural Networks, ComputerSeedsWaterZea maysEdible GrainImage Processing, Computer-AssistedWaterArtificial neural networkImage analysisMoisture contentOverall dimensionsPhysical properties

Identifiers

PMID38778037
PMCPMC11111774

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.