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End of preview. Expand in Data Studio
Horseradish Weed Detection
The dataset provides real-world RGB images capturing horseradish plants and weeds in agricultural field environments. It was collected under natural farming conditions to support object detection research for crop and weed identification in real-world agricultural settings. The dataset contains 2,697 images with 25,298 bounding box annotations across 2 categories.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
The original train/test/val split has been preserved in the split column.
Citation
@article{pagadala2026evaluation,
title={Evaluation of YOLO-based weed detection models on commercial horseradish fields in Southern Illinois},
author={Pagadala, Abhinav and Poudel, Sandesh and Rathi, Janmejay Umakanth and Sunoj, S and Reid, John F},
journal={Frontiers in Agronomy},
volume={8},
pages={1777087},
year={2026},
publisher={Frontiers Media SA}
}
Pagadala, Abhinav; Shajahan, Sunoj (2026), “Horseradish and weed dataset from commercial fields in Southern Illinois”, Mendeley Data, V1, doi: 10.17632/fcf7brsfm6.1
This dataset was reformatted from its original format to match HuggingFace standards.
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