Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/27921
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dc.contributor.authorOlaniyi, Olayemi Mikail-
dc.contributor.authorSalaudeen, Muhammadu Tajudeen-
dc.contributor.authorDaniya, Emmanuel-
dc.contributor.authorAbdullahi, Ibrahim Mohammed-
dc.contributor.authorFolorunso, Taliha Abiodun-
dc.contributor.authorBala, Jibril Abdullahi-
dc.contributor.authorNuhu, Bello Kontagora-
dc.contributor.authorAdedigba, Adeyinka Peace-
dc.contributor.authorOluwole, Blessing Israel-
dc.contributor.authorBankole, Abdullah Oreoluwa-
dc.contributor.authorMacarthy, Odunayo Moses-
dc.date.accessioned2024-05-05T15:53:29Z-
dc.date.available2024-05-05T15:53:29Z-
dc.date.issued2023-03-01-
dc.identifier.citationOlayemi 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, Odunayo Moses Macarthy, (2023), Development of maize plant dataset for intelligent recognition and weed control. Data in Brief, 109030, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.109030.en_US
dc.identifier.issn2352-3409-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/27921-
dc.description.abstractArticle history: Received 4 December 2022 Revised 22 February 2023 Accepted 23 February 2023 Available online 1 March 2023 Dataset link: Maize-Weed Image Dataset (Original data) Keywords: Maize images 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 im- ages captured with a high-resolution digital camera during the weed survey and 500 images annotated with the La- belmg 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 cam- era in each location. This dataset will serve as a benchmark Precision agriculture Autonomous robot Herbicides for computer vision and machine learning tasks in the intel- ligent maize and weed recognition research.en_US
dc.description.sponsorshipThese authors gratefully acknowledge the support of the 2020 TETFUND National Research Fund Grant Code: TETF/ES/DR&D-CE/ NRF 2020/SETI/26/VOL.1en_US
dc.language.isoenen_US
dc.publisherData in Briefen_US
dc.relation.ispartofseries109030;-
dc.subjectMaize images Precision agriculture Autonomous robot Herbicidesen_US
dc.titleDevelopment of maize plant dataset for intelligent recognition and weed control.en_US
dc.typeArticleen_US
Appears in Collections:Computer Engineering

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