Please use this identifier to cite or link to this item: http://ir.futminna.edu.ng:8080/jspui/handle/123456789/18782
Title: Development of maize plant dataset for intelligent recognition and weed control
Authors: Olaniyi, Olayemi Mikail
Salaudeen, Muhammad Tajudeen
Daniya, Emmanuel
Abdullahi, Ibrahim M.
Folorunso, Taliha Abiodun
Bala, Jibril Abdullahi
Nuhu, Bello Kontagora
Adedigba, Adeyinka Peace
Oluwole, B. I.
Bankole, Abdullah O.
Macarthy, Odunayo Moses
Keywords: Maize Images
Precision Agriculture
Autonomous Robot
Herbicides
Issue Date: 2023
Publisher: Data in Brief
Citation: Olaniyi, O. M., Salaudeen, M. T., Daniya, E., Abdullahi, I. M., Folorunso, T. A., Bala, J. A., Nuhu, B.K., Adedigba, A. P., Oluwole, B. I., Bankole, A. O., and Macarthy, O. M. (2023). Development of maize plant dataset for intelligent recognition and weed control. Data in Brief, 47 (2023), 109030. https://doi.org/10.1016/j.dib.2023.109030
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 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 for computer vision and machine learning tasks in the intel- ligent maize and weed recognition research.
URI: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/18782
Appears in Collections:Mechatronics Engineering

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