Object Detection


WeedCrop Computer Vision Project

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Explore Dataset

This dataset is derived by the following publication:

Kaspars Sudars, Janis Jasko, Ivars Namatevs, Liva Ozola, Niks Badaukis, Dataset of annotated food crops and weed images for robotic computer vision control, Data in Brief, Volume 31, 2020, 105833, ISSN 2352-3409, ( Abstract: Weed management technologies that can identify weeds and distinguish them from crops are in need of artificial intelligence solutions based on a computer vision approach, to enable the development of precisely targeted and autonomous robotic weed management systems. A prerequisite of such systems is to create robust and reliable object detection that can unambiguously distinguish weed from food crops. One of the essential steps towards precision agriculture is using annotated images to train convolutional neural networks to distinguish weed from food crops, which can be later followed using mechanical weed removal or selected spraying of herbicides. In this data paper, we propose an open-access dataset with manually annotated images for weed detection. The dataset is composed of 1118 images in which 6 food crops and 8 weed species are identified, altogether 7853 annotations were made in total. Three RGB digital cameras were used for image capturing: Intel RealSense D435, Canon EOS 800D, and Sony W800. The images were taken on food crops and weeds grown in controlled environment and field conditions at different growth stages Keywords: Computer vision; Object detection; Image annotation; Precision agriculture; Crop growth and development

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite this Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ weedcrop-waifl_dataset,
    title = { WeedCrop Dataset },
    type = { Open Source Dataset },
    author = { new-workspace-csmgu },
    howpublished = { \url{ } },
    url = { },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { jul },
    note = { visited on 2023-12-01 },

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Last Updated

a year ago

Project Type

Object Detection



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CC BY 4.0