README.md

metadata

license: cc-by-4.0
task_categories:
  - object-detection
size_categories:
  - 1K<n<10K
language:
  - en
tags:
  - soyabean
  - soyabean-pod
  - object-detection

YOLOPOD — YOLO Soybean Pod Object Detection Dataset

A ground-level imagery dataset for soybean pod detection and counting. The dataset contains 1,448 images with 72,709 annotated pod instances (single class: pod). Images are collected from two field locations (Chongzhou and Renshou). Bounding boxes are in COCO format [x, y, width, height].

Split	Images	Annotations
train	1,448	72,709

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

Loading

from datasets import load_dataset

ds = load_dataset("Project-AgML/YOLOPOD_Soyabean_Pod_Counting_Dataset")

Citation

@article{xiang2023yolopod,
  title={YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting},
  author={Xiang, Shuning and Wang, Sijun and Xu, Min and Wang, Wei and Liu, Wei},
  journal={Plant Methods},
  volume={19},
  number={1},
  year={2023},
  publisher={Springer},
  doi={10.1186/s13007-023-00985-4}
}

Xiang, Shuning; Wang, Sijun; Xu, Min; Wang, Wei; Liu, Wei (2023), "YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting", Plant Methods, 19, doi: 10.1186/s13007-023-00985-4