Maize-Weed Image Dataset

Published: 9 November 2022| Version 2 | DOI: 10.17632/jjbfcckrsp.2
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Description

The dataset contains images of maize plants and weed species. The dataset contains 36874 images in total and is stored in four folders namely Annotated Maize-Weed Images, Data Description and Questionnaire, Dry Season Maize Weed Images, and Wet Season Maize Weed Images. The Dry Season contains 18187 images captured during the dry season farm survey, the Wet Season contains 18187 images captured during the wet season farm survey and the Annotated contains 500 annotated images selected from the Dry Season survey saved in JSON, XML, and txt format. The annotation was achieved using the Labelmg suite. The raw wet and dry seasons images have been captured using a high-resolution digital camera during the weed survey, while the annotation of the annotated image was done using the Labelmg suite. A total of 18 farm locations were visited in the North Central part of Nigeria for the data acquisition as part of an ongoing research effort on maize-weed identification in farmlands.

Files

Steps to reproduce

The dataset contains images of maize plants and weed species. The dataset contains 36874 images in total and is stored in four folders namely Annotated Maize-Weed Images, Data Description and Questionnaire, Dry Season Maize Weed Images, and Wet Season Maize Weed Images. The Dry Season contains 18187 images captured during the dry season farm survey, the Wet Season contains 18187 images captured during the wet season farm survey and the Annotated contains 500 annotated images selected from the Dry Season survey saved in JSON, XML, and txt format. The annotation was achieved using the Labelmg suite. The raw wet and dry seasons images have been captured using a high-resolution digital camera during the weed survey, while the annotation of the annotated image was done using the Labelmg suite. A total of 18 farm locations were visited in the North Central part of Nigeria for the data acquisition as part of an ongoing research effort on maize-weed identification in farmlands.

Institutions

Federal University of Technology Minna

Categories

Computer Vision, Maize, Precision Agriculture, Weed

Funding

2020 TETFUND National Research Fund, Nigeria

TETF/ES/DR&D-CE/ NRF 2020/SETI/26/VOL.1

Licence