Olaniyi Olayemi Mikail, Salaudeen Muhammadu Tajudeen, Daniya Emmanuel, Abdullahi Ibrahim Mohammed, Folorunso Taliha Abiodun, Bala Jibril Abdullahi, Nuhu Bello Kontagora, Adedigba Adeyinka Peace, Oluwole Blessing Israel, Bankole Abdullah Oreoluwa, Macarthy Odunayo Moses
Department of Computer Engineering, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Department of Crop Production, Federal University of Technology, P. M. B. 65, Minna, Niger State, Nigeria.
Data Brief. 2023 Mar 1;47:109030. doi: 10.1016/j.dib.2023.109030. eCollection 2023 Apr.
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.
本文着重于开发用于识别玉米植株和杂草种类以及精确自动对杂草施用除草剂的玉米植株数据集。该数据集包括在杂草调查期间用高分辨率数码相机拍摄的36374张图像以及用Labelmg套件标注的500张图像。在尼日利亚中北部的18个农田地点，使用高分辨率相机拍摄了包含玉米植株及其相关杂草的图像。该数据集将作为智能玉米和杂草识别研究中计算机视觉和机器学习任务的基准。
