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Weed Detection in Soybean Crops
2D Polygon
Agriculture
|...
License: CC BY-NC 3.0

Overview

From the set of images captured by the UAV, all those with occurrence of weeds were selected
resulting a total of 400 images. Through the Pynovisão software, using the SLIC algorithm,
these images were segmented and the segments annotated manually with their respective class.
These segments were used in the construction of the image dataset.

Citation

@article{dos2017weed,
  title={Weed detection in soybean crops using ConvNets},
  author={dos Santos Ferreira, Alessandro and Freitas, Daniel Matte and da Silva, Gercina Gon{\c{c}}alves
and Pistori, Hemerson and Folhes, Marcelo Theophilo},
  journal={Computers and Electronics in Agriculture},
  volume={143},
  pages={314--324},
  year={2017},
  publisher={Elsevier}
}

License

CC BY-NC 3.0

Data Summary
Type
Image,
Amount
15.336K
Size
2.37GB
Provided by
Alessandro dos Santos Ferreira
| Amount 15.336K | Size 2.37GB
Weed Detection in Soybean Crops
2D Polygon
Agriculture
License: CC BY-NC 3.0

Overview

From the set of images captured by the UAV, all those with occurrence of weeds were selected
resulting a total of 400 images. Through the Pynovisão software, using the SLIC algorithm,
these images were segmented and the segments annotated manually with their respective class.
These segments were used in the construction of the image dataset.

Citation

@article{dos2017weed,
  title={Weed detection in soybean crops using ConvNets},
  author={dos Santos Ferreira, Alessandro and Freitas, Daniel Matte and da Silva, Gercina Gon{\c{c}}alves
and Pistori, Hemerson and Folhes, Marcelo Theophilo},
  journal={Computers and Electronics in Agriculture},
  volume={143},
  pages={314--324},
  year={2017},
  publisher={Elsevier}
}

License

CC BY-NC 3.0

0
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