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 |
Files in This Item:
File | Description | Size | Format | |
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1-s2.0-S2352340923001488-main.pdf | 526.31 kB | Adobe PDF | View/Open |
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