Project-AgML/greenhouse_crop_weed_classification

Greenhouse Crop Weed Classification

A dataset for classification of crops and weeds in a greenhouse setting. The dataset contains 28,000 images across 14 classes:
Images per class:

• Blackbean: 2,000
• Canola: 2,000
• Corn: 2,000
• Field Pea: 2,000
• Flax: 2,000
• Horseweed: 2,000
• Kochia: 2,000
• Lentil: 2,000
• Palmer Amaranth: 2,000
• Ragweed: 2,000
• Redroot Pigweed: 2,000
• Soybean: 2,000
• Sugar beet: 2,000
• Waterhemp: 2,000

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

Citation

@article{sunil2024novel,
  title={A novel automated cloud-based image datasets for high throughput phenotyping in weed classification},
  author={Sunil, GC and Koparan, Cengiz and Upadhyay, Arjun and Ahmed, Mohammed Raju and Zhang, Yu and Howatt, Kirk and Sun, Xin},
  journal={Data in Brief},
  volume={57},
  pages={111097},
  year={2024},
  publisher={Elsevier}
}

G C, Sunil; Koparan, Cengiz; Upadhyay, Arjun; Ahmed, Mohammed Raju ; Zhang, Yu ; Howatt, Kirk; Sun, Xin (2024), “A Novel Automated Cloud-Based Image Datasets for High Throughput Phenotyping in Weed Identification.”, Mendeley Data, V3, doi: 10.17632/hs7d7kpd3z.3

This dataset was reformatted from its original format to match HuggingFace standards.