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https://github.com/xml94/PPDRD
Collect public plant disease recognition datasets
https://github.com/xml94/PPDRD
Last synced: 7 days ago
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Collect public plant disease recognition datasets
- Host: GitHub
- URL: https://github.com/xml94/PPDRD
- Owner: xml94
- Created: 2023-10-25T06:43:46.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-11T01:02:02.000Z (8 months ago)
- Last Synced: 2024-08-02T15:22:41.331Z (3 months ago)
- Size: 7.21 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# PPDRD
**New contribution is encouraged and appreciated.**
**Collect public plant disease recognition datasets.**
This project aims to **only** collect the public dataset to recognize plant disease because the community can not
verify the performance on the private ones, although they have information and contributions.
* For every dataset, a file is linked to give more description.
* Dataset name: we will give a name if a dataset has no name. Default is fully public and PartPublic means partially public.
* Crop: show the crop if only one or give the number of crops, otherwise.
* Number of classes: include diseased classes and healthy if having.
* Number of images: **Only** those images with public labels are counted, and **only** the original images are counted (augmented images are not).
* Image background: complex (cmpx), medium (med), simple (simp).
* Machine learning (ML) task: image classification (clf), object detection (obj), segmentation (seg).
* Performance (PE): official leaderboard for challenges, or reported results in the dataset publication.
* Reference: default is with publications or official challenges such as in kaggle; otherwise no reference .
| Dataset name | Crop | Class | Image | Image BG | ML task & PE |
|----------------------------------------------|:----------|------:|-------:|:-------------|:-----------------|
| [Apple2020][Apple2020] | Apple | 4 | 1,821 | med | clf: 0.984 AUROC |
| [Apple2021][Apple2021] | Apple | 6 | 18,632 | med | clf: 0.883 F1 |
| [PCApple2023][PCApple2023] | Apple | 9 | 10,212 | med+sim | clf: N.A |
| [ASDID][ASDID] | Soybean | 8 | 9,648 | med+sim | clf: 0.968 Acc |
| [BRACOL][BRACOL] | Coffee | 5 | 1,747 | sim | clf: 0.956 Acc |
| [RoCoLe][RoCoLe] | Coffee | 6 | 1,560 | med | clf: N.A |
| [iCassava][iCassava] | Cassava | 5 | 5,656 | med | clf: 0.939 Acc |
| [CLDCMakerere][CLDCMakerere] | Cassava | 5 | 21,397 | cmpx+med | clf: 0.913 Acc |
| [CLDCAmanda][CLDCAmanda] | Cassava | 6 | 2,249 | med | clf: 0.930 Acc |
| [CLDD][CLDD] | Cassava | 3 | 228 | med | clf: N.A |
| [CDRD][CDRD] | Cucumber | 8 | 1,289 | med+sim | clf: N.A |
| [CucumberNegm][CucumberNegm] | Cucumber | 2 | 691 | med | clf: N.A |
| [PaddyDoctor][PaddyDoctor] | Rice | 10 | 10,407 | cmpx | clf: 0.990 Acc |
| [Rice1426][Rice1426] | Rice | 9 | 1,426 | cmpx+med+sim | clf: 0.971 Acc |
| [Rice5932][Rice5932] | Rice | 4 | 5,932 | med | clf: 0.984 Acc |
| [HuyDoRice][HuyDoRice] | Rice | 4 | 3,355 | sim | clf: 0.984 Acc |
| [DhanShomadhan](DhanShomadhan) | Rice | 5 | 1,106 | cmpx+sim | clf: N.A |
| [WheatLong][WheatLong] | Wheat | 5 | 999 | cmpx | clf: 0.971 Acc |
| [WheatLeafDataset][WheatLeafDataset] | Wheat | 3 | 407 | med+sim | clf: N.A |
| [GroundNutLeaf][GroundNutLeaf] | Groundnut | 5 | 3,058 | med | clf: N.A |
| [MaizeCraze][MaizeCraze] | Corn | 6 | 2,355 | sim | clf: N.A |
| [BisqueCorn][BisqueCorn] | Corn | 2 | 1,785 | cmpx | clf: N.A |
| [CornNLB][CornNLB] | Corn | 1 | 18,222 | cmpx | clf: N.A |
| [iBean][iBean] | Bean | 3 | 1,296 | med | clf: N.A |
| [SoybeanMignoni][SoybeanMignoni] | Soybean | 3 | 6,410 | cmpx | clf: N.A |
| [TaiwanTomato][TaiwanTomato] | Tomato | 6 | 622 | med+sim | clf: N.A |
| [GLFD][GLFD] | Guava | 5 | 527 | sim | clf: N.A |
| [IDADP][IDADP] | Grape | 7 | 3,596 | cmpx | clf: N.A |
| [CitrusRauf][CitrusRauf] | Citrus | 10 | 759 | sim | clf: N.A |
| [PlantVillage][PlantVillage] | 14 | 38 | 54,305 | sim | clf: N.A |
| [FieldPV][FieldPV] | 14 | 38 | 665 | med+sim | clf: 0.720 Acc |
| [PlantDocCls][PlantDocCls] | 13 | 27 | 2,598 | cmpx+med+sim | clf: N.A |
| [PlantConservation][PlantConservation] | 12 | 10 | 4,503 | sim | clf: N.A |
| [CCMT][CCMT] | 4 | 22 | 24,881 | med+sim | clf: N.A |
| [PDD271][PDD271] | N.A | 271 | 2,710 | cmpx+med | clf: 0.855 Acc |
| [PlantDocObj][PlantDocCls] | 13 | 27 | 2,598 | cmpx+med+sim | obj: N.A |
| [NZDLPlantDiseaseV1][NZDLPlantDiseaseV1] | 5 | 20 | 3,337 | med | obj: 0.745 mAP |
| [NZDLPlantDiseaseV2][NZDLPlantDiseaseV2] | 8 | 28 | 3,039 | med | obj: 0.932 mAP |
| [FieldPlant][FieldPlant] | 4 | 31 | 5,156 | cmpx+med | obj: 0.144 mAP |
| [GrapevineDiseaseMalo][GrapevineDiseaseMalo] | Grape | 3 | 744 | cmpx | obj: N.A |
| [GrapevineDiseaseMalo][GrapevineDiseaseMalo] | Grape | 4 | 128 | cmpx | seg: N.A |
| [RoCoLe][RoCoLe] | Coffee | 2 | 1,560 | sim | seg: N.A |
| [ATLDSD][ATLDSD] | Apple | 5 | 1,641 | med+sim | seg: N.A |
[//]: # (| | | | | | |)
[Apple2020]: https://github.com/xml94/PPDRD/tree/main/data/Apple2020.md
[Apple2021]: https://github.com/xml94/PPDRD/tree/main/data/Apple2021.md
[ASDID]: https://github.com/xml94/PPDRD/tree/main/data/ASDID.md
[BRACOL]: https://github.com/xml94/PPDRD/tree/main/data/BRACOL.md
[PCApple2023]: https://github.com/xml94/PPDRD/tree/main/data/PCApple2023.md
[CLDCMakerere]: https://github.com/xml94/PPDRD/tree/main/data/CLDCMakerere.md
[CDRD]: https://github.com/xml94/PPDRD/tree/main/data/CDRD.md
[DhanShomadhan]: https://github.com/xml94/PPDRD/tree/main/data/DhanShomadhan.md
[IndonesiaRice240]: https://github.com/xml94/PPDRD/tree/main/data/IndonesiaRice240.md
[PaddyDoctor]: https://github.com/xml94/PPDRD/tree/main/data/PaddyDoctor.md
[Rice1426]: https://github.com/xml94/PPDRD/tree/main/data/Rice1426.md
[Rice5932]: https://github.com/xml94/PPDRD/tree/main/data/Rice5932.md
[WheatLong]: https://github.com/xml94/PPDRD/tree/main/data/WheatLong.md
[DhanShomadhan]: https://github.com/xml94/PPDRD/tree/main/data/DhanShomadhan.md
[MaizeCraze]: https://github.com/xml94/PPDRD/tree/main/data/MaizeCraze.md
[iCassava]: https://github.com/xml94/PPDRD/tree/main/data/iCassava.md
[PlantVillage]: https://github.com/xml94/PPDRD/tree/main/data/PlantVillage.md
[PlantConservation]: https://github.com/xml94/PPDRD/tree/main/data/PlantConservation.md
[CCMT]: https://github.com/xml94/PPDRD/tree/main/data/CCMT.md
[PlantDocCls]: https://github.com/xml94/PPDRD/tree/main/data/PlantDoc.md
[FieldPV]: https://github.com/xml94/PPDRD/tree/main/data/FieldPV.md
[GroundNutLeaf]: https://github.com/xml94/PPDRD/tree/main/data/GroundNutLeaf.md
[CLDD]: https://github.com/xml94/PPDRD/tree/main/data/CLDD.md
[CLDCAmanda]: https://github.com/xml94/PPDRD/tree/main/data/CLDCAmanda.md
[HuyDoRice]: https://github.com/xml94/PPDRD/tree/main/data/HuyDoRice.md
[iBean]: https://github.com/xml94/PPDRD/tree/main/data/iBean.md
[BisqueCorn]: https://github.com/xml94/PPDRD/tree/main/data/BisqueCorn.md
[RoCoLe]: https://github.com/xml94/PPDRD/tree/main/data/RoCoLe.md
[WheatLeafDataset]: https://github.com/xml94/PPDRD/tree/main/data/WheatLeafDataset.md
[TaiwanTomato]: https://github.com/xml94/PPDRD/tree/main/data/TaiwanTomato.md
[SoybeanMignoni]: https://github.com/xml94/PPDRD/tree/main/data/SoybeanMignoni.md
[GLFD]: https://github.com/xml94/PPDRD/tree/main/data/GLFD.md
[GFLDRauf]: https://github.com/xml94/PPDRD/tree/main/data/GFLDRauf.md
[CucumberNegm]: https://github.com/xml94/PPDRD/tree/main/data/CucumberNegm.md
[CitrusRauf]: https://github.com/xml94/PPDRD/tree/main/data/CitrusRauf.md
[ATLDSD]: https://github.com/xml94/PPDRD/tree/main/data/ATLDSD.md
[CornNLB]: https://github.com/xml94/PPDRD/tree/main/data/CornNLB.md
[PDD271]: https://github.com/xml94/PPDRD/tree/main/data/PDD271.md
[IDADP]: https://github.com/xml94/PPDRD/tree/main/data/IDADP.md
[NZDLPlantDiseaseV1]: https://github.com/xml94/PPDRD/tree/main/data/NZDLPlantDiseaseV1.md
[NZDLPlantDiseaseV2]: https://github.com/xml94/PPDRD/tree/main/data/NZDLPlantDiseaseV2.md
[FieldPlant]: https://github.com/xml94/PPDRD/tree/main/data/FieldPlant.md
[GrapevineDiseaseMalo]: https://github.com/xml94/PPDRD/tree/main/data/GrapevineDiseaseMalo.md
# Reference
Please consider cite our related papers if you think this project is useful.
```angular2html
@article{xu2023plant,
title={Plant Disease Recognition Datasets in the Age of Deep Learning: Challenges and Opportunities},
author={Xu, Mingle and Park, Ji Eun and Lee, Jaehwan and Yang, Jucheng and Yoon, Sook},
journal={arXiv preprint arXiv:2312.07905},
year={2023}
}
@article{meng2023known,
title={Known and unknown class recognition on plant species and diseases},
author={Meng, Yao and Xu, Mingle and Kim, Hyongsuk and Yoon, Sook and Jeong, Yongchae and Park, Dong Sun},
journal={Computers and Electronics in Agriculture},
volume={215},
pages={108408},
year={2023},
publisher={Elsevier}
}
@article{xu2023embracing,
title={Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning},
author={Xu, Mingle and Kim, Hyongsuk and Yang, Jucheng and Fuentes, Alvaro and Meng, Yao and Yoon, Sook and Kim, Taehyun and Park, Dong Sun},
journal={Frontiers in Plant Science},
volume={14},
year={2023},
publisher={Frontiers Media SA}
}
@article{xu2022transfer,
title={Transfer learning for versatile plant disease recognition with limited data},
author={Xu, Mingle and Yoon, Sook and Jeong, Yongchae and Park, Dong Sun},
journal={Frontiers in Plant Science},
volume={13},
pages={1010981},
year={2022},
publisher={Frontiers}
}
```