---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': '0'
            '1': '1'
            '2': '2'
            '3': '3'
            '4': '4'
            '5': '5'
            '6': '6'
            '7': '7'
            '8': '8'
            '9': '9'
            '10': '10'
            '11': '11'
            '12': '12'
            '13': '13'
            '14': '14'
  splits:
  - name: train
    num_bytes: 2759067041
    num_examples: 6656
  download_size: 2969930573
  dataset_size: 2759067041
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 1K<n<10K
---


# MH Weed16 Weed Detection

A dataset for weed detection in Soybean fields. The dataset contains 6,656 images with 62,052 bounding box annotations across 15 categories.

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

## Citation

```bibtex
@article{shinde2025indian,
  title={An Indian annotated weed dataset for computer vision tasks in precision farming},
  author={Shinde, Sayali and Attar, Vahida},
  journal={Data in Brief},
  volume={61},
  pages={111691},
  year={2025},
  publisher={Elsevier}
}
```

Shinde, Sayali; Attar, Dr. Vahida; Technological University Pune, COEP; Technology Innovation Hub, Indian Statistical Institute Kolkata, IDEAS (2025), “MH-Weed16:An Indian Multiclass Annotated Weed Dataset for Computer Vision Tasks ”, Mendeley Data, V2, doi: 10.17632/d3n3mgjjbv.2