Datasets:
image imagewidth (px) 640 640 | objects dict | split stringclasses 3
values |
|---|---|---|
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... | train | |
{
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{
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} | train |
End of preview. Expand in Data Studio
Tea Leaf Disease Pest Detection
This dataset provides real-world RGB images of tea leaves affected by various diseases and pests, captured directly in agricultural field conditions. It offers a field-realistic resource for developing and testing object detection models in precision agriculture applications for tea crop monitoring. The dataset contains 9,591 images with 12,812 bounding box annotations across 8 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{lyu2026spatial,
title={Spatial-aware lightweight network for real-time tea disease detection: A coordinate attention-enhanced YOLOv8n approach with path-decoupling strategy},
author={Lyu, Xiang and Yu, Yue and Song, ChengLei},
journal={PLOS One},
volume={21},
pages={e0354583},
year={2026},
publisher={Public Library of Science}
}
The dataset itself can be cited as:
lv, xiang, & ChengLei, S. (2026). Tea-Leaf Disease and Pest Detection Dataset (v1.0) (Version 1) [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.32253357.V1
This dataset was reformatted from its original format to match HuggingFace standards.
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