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

Autor:	Olaniyi OM; Department of Computer Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria., Salaudeen MT; Department of Crop Production, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria., Daniya E; Department of Crop Production, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria., Abdullahi IM; Department of Computer Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria., Folorunso TA; Department of Mechatronics Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria., Bala JA; Department of Mechatronics Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria., Nuhu BK; Department of Computer Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria., Adedigba AP; Department of Mechatronics Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria., Oluwole BI; Department of Crop Production, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria., Bankole AO; Department of Mechatronics Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria., Macarthy OM; Department of Mechatronics Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Jazyk:	angličtina
Zdroj:	Data in brief [Data Brief] 2023 Mar 01; Vol. 47, pp. 109030. Date of Electronic Publication: 2023 Mar 01 (Print Publication: 2023).
DOI:	10.1016/j.dib.2023.109030
Abstrakt:	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. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2023 The Author(s).)
Databáze:	MEDLINE
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