P2PNet-Soy

Soybean seed localization and counting based on dot annotated infield images.

Dataset

Please fill this form to get download link of labeled dataset and test dataset.

source code of original P2PNet

It is heavily dependent on the original P2PNet: https://github.com/TencentYoutuResearch/CrowdCounting-P2PNet

@inproceedings{song2021rethinking,
  title={Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework},
  author={Song, Qingyu and Wang, Changan and Jiang, Zhengkai and Wang, Yabiao and Tai, Ying and Wang, Chengjie and Li, Jilin and Huang, Feiyue and Wu, Yang},
  journal={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2021}
}  

source code of P2PNet-soy

[Updates at 2025]: Please try our lastest EasyP2PNet with much more user friendly API and supports for multi-class. But the demo data is grain counting not soybean counting.

Or run original P2PNet-Soy code with your own data on Colab: here

Please cite this paper if you like it：

@Article{zhao_P2PNet-Soy_2022,
AUTHOR = { Zhao, Jiangsan and Kaga,Akito and Hirafuji,Masayuki and Ninomiya,Seishi and Guo, Wei},
TITLE = {Improved infield soybean seed counting and localization with feature level considered},
JOURNAL = {PLANT PHENOMICS},
VOLUME = {},
YEAR = {},
NUMBER = {},
URL = {},
ISSN = {},
DOI = {10.34133/plantphenomics.0026}
}

Got the first place in MLCAS2022 Soybean Pod Counting Challenge (https://mlcas2022.github.io/) using similar strategies:

GitHub link to the dataset and relevant scripts can be found at : here