Overview

This is repository for our Nature Scientific Data paper: An annotated grain kernel image database for visual quality inspection. (DOI: https://doi.org/10.1038/s41597-023-02660-8)

Relased Datasets

We released four types of cereal grains: Wheat, Maize, Sorghum and Rice in single-kernel images with experts' annotations. Additionally,

• GrainSet-tiny: this is a preview for understanding our database by randomly selecting 2% samples from GrainSet.
• GrainSet-raw: this is a reference for understanding the data acquisition and pre-processing procedures by randomly selecting 5% raw images captured by our acquisition device.

Species	Num.	URL
Wheat	200K	https://doi.org/10.6084/m9.figshare.22992317.v2
Maize	19K	https://doi.org/10.6084/m9.figshare.22987562.v2
Sorghum	102K	https://doi.org/10.6084/m9.figshare.22988981.v2
Rice	31K	https://doi.org/10.6084/m9.figshare.22987292.v3
GrainSet-tiny	6.5K	https://doi.org/10.6084/m9.figshare.22989029.v1
GrainSet-raw	15K	https://doi.org/10.6084/m9.figshare.24137472.v1

Validation

1.Prepare Datasets

• unzip wheat/maize/sorghum/rice.zip to /your/data/path
• download datalist.zip from datasets folder
• unzip datalist.zip to runs/datalist

2.Train deep learning-based Models

• set data_path`` and CUDA_VISIBLE_DEVICES`` in .sh files
• run shell scripts, e.g.: bash run_res50.sh

3.Train traditional SVM

• extract features: python src/extract_feature.py
• train svm classifier: python src/svm_train_test.py
• library supports:

      python==3.7     
      opencv-contrib-python==3.4.2.17     
      opencv-python==3.4.2.17

4.Test

• set your model path and data path in src/test.py
• run test: python src/test.py

others

• plot figures: python src/plot.py

Citation

If our paper has been of assistance, we would appreciate it if you could consider citing it in your work.

@article{fan2023annotated,
  title={An annotated grain kernel image database for visual quality inspection},
  author={Fan, Lei and Ding, Yiwen and Fan, Dongdong and Wu, Yong and Chu, Hongxia and Pagnucco, Maurice and Song, Yang},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={778},
  year={2023},
  publisher={Nature Publishing Group UK London}
}


@inproceedings{fan2022grainspace,
  title={GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains},
  author={Fan, Lei and Ding, Yiwen and Fan, Dongdong and Di, Donglin and Pagnucco, Maurice and Song, Yang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={21116--21125},
  year={2022}
}


@incollection{fan2023ai4graininsp,
  title={Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection},
  author={Fan, Lei and Ding, Yiwen and Fan, Dongdong and Wu, Yong and Pagnucco, Maurice and Song, Yang},
  booktitle={ECAI 2023},
  year={2023},
  publisher={IOS Press}
}


@article{fan2023av4gainsp,
  title={AV4GAInsp: An Efficient Dual-Camera System for Identifying Defective Kernels of Cereal Grains},
  author={Fan, Lei and Ding, Yiwen and Fan, Dongdong and Wu, Yong and Chu, Hongxia and Pagnucco, Maurice and Song, Yang},
  journal={IEEE Robotics and Automation Letters},
  year={2023},
  publisher={IEEE}
}