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(TGRS'22) TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression

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Maize Tassels Counting from Unmanned Aerial Vehicle (MTC-UAV)

This repository hosts the MTC-UAV dataset associated with our work:

TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression

Hao Lu1, Liang Liu1,, Ya-Nan Li2, Xiao-Ming Zhao3, Xi-Qing Wang3, Zhi-Guo Cao1

1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, China

2School of Computer Science and Engineering, Wuhan Institute of Technology, China

3Crop Functional Genomics and Molecular Breeding, College of Biological Science, China Agricultural University, China

IEEE Transactions on Geoscience and Remote Sensing, 2021

Highlights

  • over 400 maize cultivars
  • 70,870 manually annotated instances
  • 306 images with 5472x3648 image resolution
  • 12.5-meter-height nadir imaging view

Download

The MTC-UAV dataset can be downloaded from:

Google Drive (3.8 GB)

Path Structure

Suppose that the dataset is placed in the ./data folder, the path structure should look like:

$./data/maize_tassels_counting_uav_dataset
├──── images
│       ├──── DJI_0002.JPG
│       ├──── DJI_0007.JPG
│       ├──── ...
├──── labels
│       ├──── DJI_0002.csv
│       ├──── DJI_0007.csv
│       ├──── ...
├──── train.txt
├──── val.txt
  • train.txt and val.txt record the train/val split used in the experiments.

Citation

If you find this work or code useful for your research, please cite:

@article{lu2021tasselnetv3,
  title={TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression},
  author={Lu, Hao and Liu, Liang and Li, Ya-Nan and Zhao, Xiao-Ming and Wang, Xi-Qing and Cao, Zhiguo},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2021}
}

Permission

This dataset is only for academic purposes. Contact Hao Lu (hlu@hust.edu.cn) or Zhiguo Cao (zgcao@hust.edu.cn) for the commerial use of the model.

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(TGRS'22) TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression

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