Evidence map›Paper›PMID 36936631›Full record

ArticleData in brief2023

Development of maize plant dataset for intelligent recognition and weed control.

Olayemi Mikail Olaniyi, Muhammadu Tajudeen Salaudeen, Emmanuel Daniya, Ibrahim Mohammed Abdullahi, Taliha Abiodun Folorunso, Jibril Abdullahi Bala, Bello Kontagora Nuhu, Adeyinka Peace Adedigba, Blessing Israel Oluwole, Abdullah Oreoluwa Bankole and 1 more

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Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Olayemi Mikail OlaniyiDepartment of Computer Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Muhammadu Tajudeen SalaudeenDepartment of Crop Production, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Emmanuel DaniyaDepartment of Crop Production, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Ibrahim Mohammed AbdullahiDepartment of Computer Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Taliha Abiodun FolorunsoDepartment of Mechatronics Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Jibril Abdullahi BalaDepartment of Mechatronics Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Bello Kontagora NuhuDepartment of Computer Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Adeyinka Peace AdedigbaDepartment of Mechatronics Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Blessing Israel OluwoleDepartment of Crop Production, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Abdullah Oreoluwa BankoleDepartment of Mechatronics Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Odunayo Moses MacarthyDepartment of Mechatronics Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper focuses on the development of maize plant datasets for the purposes of recognizing maize plants and weed species, as well as the precise automated application of herbicides to the weeds. The dataset includes 36,374 images captured with a high-resolution digital camera during the weed survey and 500 images annotated with the Labelmg suite. Images of the eighteen farmland locations in North Central Nigeria, containing the maize plants and their associated weeds were captured using a high-resolution camera in each location. This dataset will serve as a benchmark for computer vision and machine learning tasks in the intelligent maize and weed recognition research.

Indexed as

Autonomous robotHerbicidesMaize imagesPrecision agriculture

Identifiers

PMID36936631
PMCPMC10018041

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LicenceCC BY
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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.