Assessment of off-road agricultural traction in situ using large scale machine learning and neurocomputing models.

Frankline Mwiti, Ayub Gitau, Duncan Mbuge, Ruth Njoroge, Diogenes L Antille, Jitendra Khatti

Abstract read

In one paragraph

Article in Scientific reports, 2025. 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

6 authors.

Frankline MwitiDepartment of Environmental and Biosystems Engineering, Machinery Section, University of Nairobi, P.O.Box 30197, 00100, Nairobi, Kenya. franklinemwiti2@gmail.com.ORCID https://orcid.org/0000-0002-9662-9430

Ayub GitauFaculty of Engineering, University of Nairobi, P.O.Box 30197, 00100, Nairobi, Kenya.

Duncan MbugeFaculty of Engineering, University of Nairobi, P.O.Box 30197, 00100, Nairobi, Kenya.ORCID https://orcid.org/0000-0002-3943-2877

Ruth NjorogeDepartment of Soil Science, University of Eldoret, P.O.Box 1125, 30100, Eldoret, Kenya.ORCID https://orcid.org/0000-0001-6102-5795

Diogenes L AntilleCSIRO Agriculture and Food, Canberra, Australian Capital Territory 2601, Australia.ORCID https://orcid.org/0000-0002-4924-9689

Jitendra KhattiDepartment of Civil Engineering, Rajasthan Technical University, Kota, Rajasthan, 324010, India.ORCID https://orcid.org/0000-0001-9939-4844

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial neuro-cognitive models can simulate human brain intelligence to enable accurate decision-making during complex agricultural operations in situ. To investigate this, twelve machine learning algorithms were employed to sequentially train 72 neurocomputing architectures using Deep Neural Network (DNN) and Artificial Neural Network (ANN) models for the neurocognitive prediction of tractive force (F

Indexed as

AgricultureMachine LearningAlgorithmsBayes TheoremHumansNeural Networks, ComputerArtificial intelligenceAutonomous tractorNeurocognitive equationPuma optimizerSpider wasp optimizationTillage robotWalrus optimization

Identifiers

PMID41006420

DOI10.1038/s41598-025-17736-1

PMCPMC12475496

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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.