Weed Detection in Soybean Crops
2D Polygon
Agriculture
|...
许可协议: 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

数据概要
数据格式
Image,
数据量
15.336K
文件大小
2.37GB
发布方
Alessandro dos Santos Ferreira
数据集反馈
| 101 | 数据量 15.336K | 大小 2.37GB
Weed Detection in Soybean Crops
2D Polygon
Agriculture
许可协议: 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

数据集反馈
0
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