├── .gitattributes ├── .idea ├── .gitignore ├── PPDRD.iml ├── deployment.xml ├── inspectionProfiles │ └── profiles_settings.xml ├── misc.xml ├── modules.xml └── vcs.xml ├── README.md ├── TBD └── plantwild.md └── data ├── ASDID.md ├── ATLDSD.md ├── Apple2020.md ├── Apple2020.png ├── Apple2021.md ├── BRACOL.md ├── BisqueCorn.md ├── CCMT.md ├── CDRD.md ├── CLDCAmanda.md ├── CLDCMakerere.md ├── CLDD.md ├── CitrusRauf.md ├── CornNLB.md ├── CucumberNegm.md ├── DhanShomadhan.md ├── FieldPV.md ├── FieldPlant.md ├── GFLDRauf.md ├── GLFD.md ├── GrapevineDiseaseMalo.md ├── GroundNutLeaf.md ├── HuyDoRice.md ├── IDADP.md ├── MaizeCraze.md ├── NZDLPlantDiseaseV1.md ├── NZDLPlantDiseaseV2.md ├── PCApple2023.md ├── PDD271.md ├── PDDM.md ├── PaddyDoctor.md ├── PlantConservation.md ├── PlantDoc.md ├── PlantVillage.md ├── Rice1426.md ├── Rice5932.md ├── RoCoLe.md ├── SoybeanMignoni.md ├── TaiwanTomato.md ├── WheatLeafDataset.md ├── WheatLong.md ├── demo.md ├── iBean.md ├── iCassava.md └── paddy_doctor.png /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | # Editor-based HTTP Client requests 5 | /httpRequests/ 6 | # Datasource local storage ignored files 7 | /dataSources/ 8 | /dataSources.local.xml 9 | -------------------------------------------------------------------------------- /.idea/PPDRD.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/deployment.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PPDRD 2 | **New contribution is encouraged and appreciated.** 3 | 4 | **Collect public plant disease recognition datasets.** 5 | 6 | This project aims to **only** collect the public dataset to recognize plant disease because the community can not 7 | verify the performance on the private ones, although they have information and contributions. 8 | 9 | * For every dataset, a file is linked to give more description. 10 | * Dataset name: we will give a name if a dataset has no name. Default is fully public and PartPublic means partially public. 11 | * Crop: show the crop if only one or give the number of crops, otherwise. 12 | * Number of classes: include diseased classes and healthy if having. 13 | * Number of images: **Only** those images with public labels are counted, and **only** the original images are counted (augmented images are not). 14 | * Image background: complex (cmpx), medium (med), simple (simp). 15 | * Machine learning (ML) task: image classification (clf), object detection (obj), segmentation (seg). 16 | * Performance (PE): official leaderboard for challenges, or reported results in the dataset publication. 17 | * Reference: default is with publications or official challenges such as in kaggle; otherwise no reference ![](https://img.shields.io/badge/-NoRef-grey). 18 | 19 | 20 | | Dataset name | Crop | Class | Image | Image BG | ML task & PE | 21 | |----------------------------------------------|:----------|------:|-------:|:-------------|:-----------------| 22 | | [Apple2020][Apple2020] | Apple | 4 | 1,821 | med | clf: 0.984 AUROC | 23 | | [Apple2021][Apple2021] | Apple | 6 | 18,632 | med | clf: 0.883 F1 | 24 | | [PCApple2023][PCApple2023] | Apple | 9 | 10,212 | med+sim | clf: N.A | 25 | | [ASDID][ASDID] | Soybean | 8 | 9,648 | med+sim | clf: 0.968 Acc | 26 | | [BRACOL][BRACOL] | Coffee | 5 | 1,747 | sim | clf: 0.956 Acc | 27 | | [RoCoLe][RoCoLe] | Coffee | 6 | 1,560 | med | clf: N.A | 28 | | [iCassava][iCassava] | Cassava | 5 | 5,656 | med | clf: 0.939 Acc | 29 | | [CLDCMakerere][CLDCMakerere] | Cassava | 5 | 21,397 | cmpx+med | clf: 0.913 Acc | 30 | | [CLDCAmanda][CLDCAmanda] | Cassava | 6 | 2,249 | med | clf: 0.930 Acc | 31 | | [CLDD][CLDD] | Cassava | 3 | 228 | med | clf: N.A | 32 | | [CDRD][CDRD] | Cucumber | 8 | 1,289 | med+sim | clf: N.A | 33 | | [CucumberNegm][CucumberNegm] | Cucumber | 2 | 691 | med | clf: N.A | 34 | | [PaddyDoctor][PaddyDoctor] | Rice | 10 | 10,407 | cmpx | clf: 0.990 Acc | 35 | | [Rice1426][Rice1426] | Rice | 9 | 1,426 | cmpx+med+sim | clf: 0.971 Acc | 36 | | [Rice5932][Rice5932] | Rice | 4 | 5,932 | med | clf: 0.984 Acc | 37 | | [HuyDoRice][HuyDoRice] | Rice | 4 | 3,355 | sim | clf: 0.984 Acc | 38 | | [DhanShomadhan](DhanShomadhan) | Rice | 5 | 1,106 | cmpx+sim | clf: N.A | 39 | | [WheatLong][WheatLong] | Wheat | 5 | 999 | cmpx | clf: 0.971 Acc | 40 | | [WheatLeafDataset][WheatLeafDataset] | Wheat | 3 | 407 | med+sim | clf: N.A | 41 | | [GroundNutLeaf][GroundNutLeaf] | Groundnut | 5 | 3,058 | med | clf: N.A | 42 | | [MaizeCraze][MaizeCraze] | Corn | 6 | 2,355 | sim | clf: N.A | 43 | | [BisqueCorn][BisqueCorn] | Corn | 2 | 1,785 | cmpx | clf: N.A | 44 | | [CornNLB][CornNLB] | Corn | 1 | 18,222 | cmpx | clf: N.A | 45 | | [iBean][iBean] | Bean | 3 | 1,296 | med | clf: N.A | 46 | | [SoybeanMignoni][SoybeanMignoni] | Soybean | 3 | 6,410 | cmpx | clf: N.A | 47 | | [TaiwanTomato][TaiwanTomato] | Tomato | 6 | 622 | med+sim | clf: N.A | 48 | | [GLFD][GLFD] | Guava | 5 | 527 | sim | clf: N.A | 49 | | [IDADP][IDADP] | Grape | 7 | 3,596 | cmpx | clf: N.A | 50 | | [CitrusRauf][CitrusRauf] | Citrus | 10 | 759 | sim | clf: N.A | 51 | | [PlantVillage][PlantVillage] | 14 | 38 | 54,305 | sim | clf: N.A | 52 | | [FieldPV][FieldPV] | 14 | 38 | 665 | med+sim | clf: 0.720 Acc | 53 | | [PlantDocCls][PlantDocCls] | 13 | 27 | 2,598 | cmpx+med+sim | clf: N.A | 54 | | [PlantConservation][PlantConservation] | 12 | 10 | 4,503 | sim | clf: N.A | 55 | | [CCMT][CCMT] | 4 | 22 | 24,881 | med+sim | clf: N.A | 56 | | [PDD271][PDD271] | N.A | 271 | 2,710 | cmpx+med | clf: 0.855 Acc | 57 | | [PlantDocObj][PlantDocCls] | 13 | 27 | 2,598 | cmpx+med+sim | obj: N.A | 58 | | [NZDLPlantDiseaseV1][NZDLPlantDiseaseV1] | 5 | 20 | 3,337 | med | obj: 0.745 mAP | 59 | | [NZDLPlantDiseaseV2][NZDLPlantDiseaseV2] | 8 | 28 | 3,039 | med | obj: 0.932 mAP | 60 | | [FieldPlant][FieldPlant] | 4 | 31 | 5,156 | cmpx+med | obj: 0.144 mAP | 61 | | [GrapevineDiseaseMalo][GrapevineDiseaseMalo] | Grape | 3 | 744 | cmpx | obj: N.A | 62 | | [GrapevineDiseaseMalo][GrapevineDiseaseMalo] | Grape | 4 | 128 | cmpx | seg: N.A | 63 | | [RoCoLe][RoCoLe] | Coffee | 2 | 1,560 | sim | seg: N.A | 64 | | [ATLDSD][ATLDSD] | Apple | 5 | 1,641 | med+sim | seg: N.A | 65 | 66 | [//]: # (| | | | | | |) 67 | 68 | 69 | 70 | [Apple2020]: https://github.com/xml94/PPDRD/tree/main/data/Apple2020.md 71 | [Apple2021]: https://github.com/xml94/PPDRD/tree/main/data/Apple2021.md 72 | [ASDID]: https://github.com/xml94/PPDRD/tree/main/data/ASDID.md 73 | [BRACOL]: https://github.com/xml94/PPDRD/tree/main/data/BRACOL.md 74 | [PCApple2023]: https://github.com/xml94/PPDRD/tree/main/data/PCApple2023.md 75 | [CLDCMakerere]: https://github.com/xml94/PPDRD/tree/main/data/CLDCMakerere.md 76 | [CDRD]: https://github.com/xml94/PPDRD/tree/main/data/CDRD.md 77 | [DhanShomadhan]: https://github.com/xml94/PPDRD/tree/main/data/DhanShomadhan.md 78 | [IndonesiaRice240]: https://github.com/xml94/PPDRD/tree/main/data/IndonesiaRice240.md 79 | [PaddyDoctor]: https://github.com/xml94/PPDRD/tree/main/data/PaddyDoctor.md 80 | [Rice1426]: https://github.com/xml94/PPDRD/tree/main/data/Rice1426.md 81 | [Rice5932]: https://github.com/xml94/PPDRD/tree/main/data/Rice5932.md 82 | [WheatLong]: https://github.com/xml94/PPDRD/tree/main/data/WheatLong.md 83 | [DhanShomadhan]: https://github.com/xml94/PPDRD/tree/main/data/DhanShomadhan.md 84 | [MaizeCraze]: https://github.com/xml94/PPDRD/tree/main/data/MaizeCraze.md 85 | [iCassava]: https://github.com/xml94/PPDRD/tree/main/data/iCassava.md 86 | [PlantVillage]: https://github.com/xml94/PPDRD/tree/main/data/PlantVillage.md 87 | [PlantConservation]: https://github.com/xml94/PPDRD/tree/main/data/PlantConservation.md 88 | [CCMT]: https://github.com/xml94/PPDRD/tree/main/data/CCMT.md 89 | [PlantDocCls]: https://github.com/xml94/PPDRD/tree/main/data/PlantDoc.md 90 | [FieldPV]: https://github.com/xml94/PPDRD/tree/main/data/FieldPV.md 91 | [GroundNutLeaf]: https://github.com/xml94/PPDRD/tree/main/data/GroundNutLeaf.md 92 | [CLDD]: https://github.com/xml94/PPDRD/tree/main/data/CLDD.md 93 | [CLDCAmanda]: https://github.com/xml94/PPDRD/tree/main/data/CLDCAmanda.md 94 | [HuyDoRice]: https://github.com/xml94/PPDRD/tree/main/data/HuyDoRice.md 95 | [iBean]: https://github.com/xml94/PPDRD/tree/main/data/iBean.md 96 | [BisqueCorn]: https://github.com/xml94/PPDRD/tree/main/data/BisqueCorn.md 97 | [RoCoLe]: https://github.com/xml94/PPDRD/tree/main/data/RoCoLe.md 98 | [WheatLeafDataset]: https://github.com/xml94/PPDRD/tree/main/data/WheatLeafDataset.md 99 | [TaiwanTomato]: https://github.com/xml94/PPDRD/tree/main/data/TaiwanTomato.md 100 | [SoybeanMignoni]: https://github.com/xml94/PPDRD/tree/main/data/SoybeanMignoni.md 101 | [GLFD]: https://github.com/xml94/PPDRD/tree/main/data/GLFD.md 102 | [GFLDRauf]: https://github.com/xml94/PPDRD/tree/main/data/GFLDRauf.md 103 | [CucumberNegm]: https://github.com/xml94/PPDRD/tree/main/data/CucumberNegm.md 104 | [CitrusRauf]: https://github.com/xml94/PPDRD/tree/main/data/CitrusRauf.md 105 | [ATLDSD]: https://github.com/xml94/PPDRD/tree/main/data/ATLDSD.md 106 | [CornNLB]: https://github.com/xml94/PPDRD/tree/main/data/CornNLB.md 107 | [PDD271]: https://github.com/xml94/PPDRD/tree/main/data/PDD271.md 108 | [IDADP]: https://github.com/xml94/PPDRD/tree/main/data/IDADP.md 109 | [NZDLPlantDiseaseV1]: https://github.com/xml94/PPDRD/tree/main/data/NZDLPlantDiseaseV1.md 110 | [NZDLPlantDiseaseV2]: https://github.com/xml94/PPDRD/tree/main/data/NZDLPlantDiseaseV2.md 111 | [FieldPlant]: https://github.com/xml94/PPDRD/tree/main/data/FieldPlant.md 112 | [GrapevineDiseaseMalo]: https://github.com/xml94/PPDRD/tree/main/data/GrapevineDiseaseMalo.md 113 | 114 | 115 | # Reference 116 | Please consider cite our related papers if you think this project is useful. 117 | ```angular2html 118 | @article{xu2023plant, 119 | title={Plant Disease Recognition Datasets in the Age of Deep Learning: Challenges and Opportunities}, 120 | author={Xu, Mingle and Park, Ji Eun and Lee, Jaehwan and Yang, Jucheng and Yoon, Sook}, 121 | journal={arXiv preprint arXiv:2312.07905}, 122 | year={2023} 123 | } 124 | @article{meng2023known, 125 | title={Known and unknown class recognition on plant species and diseases}, 126 | author={Meng, Yao and Xu, Mingle and Kim, Hyongsuk and Yoon, Sook and Jeong, Yongchae and Park, Dong Sun}, 127 | journal={Computers and Electronics in Agriculture}, 128 | volume={215}, 129 | pages={108408}, 130 | year={2023}, 131 | publisher={Elsevier} 132 | } 133 | @article{xu2023embracing, 134 | title={Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning}, 135 | 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}, 136 | journal={Frontiers in Plant Science}, 137 | volume={14}, 138 | year={2023}, 139 | publisher={Frontiers Media SA} 140 | } 141 | @article{xu2022transfer, 142 | title={Transfer learning for versatile plant disease recognition with limited data}, 143 | author={Xu, Mingle and Yoon, Sook and Jeong, Yongchae and Park, Dong Sun}, 144 | journal={Frontiers in Plant Science}, 145 | volume={13}, 146 | pages={1010981}, 147 | year={2022}, 148 | publisher={Frontiers} 149 | } 150 | ``` -------------------------------------------------------------------------------- /TBD/plantwild.md: -------------------------------------------------------------------------------- 1 | [plant wild github](https://tqwei05.github.io/PlantWild/) -------------------------------------------------------------------------------- /data/ASDID.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | | Key | Value | 4 | |:----------------------------|:------------------------------------------------------------------------------------------------------------------------------------| 5 | | Dataset name | ASDID![](https://img.shields.io/badge/-Public-008000) | 6 | | Publication link | [Journal: Computers and Electronics in Agriculture](https://www.sciencedirect.com/science/article/pii/S0168169922007578?via%3Dihub) | 7 | | Dataset download link | [datadry](https://datadryad.org/stash/dataset/doi:10.5061/dryad.41ns1rnj3) | 8 | | Year | 2022 Nov 8 | 9 | | Imaging location | Alabama, USA | 10 | | Agricultural task | Disease recognition | 11 | | Machine learning task | Image classification | 12 | | Machine learning challenge | | 13 | | Metric and performance | Acc: 0.968 | 14 | | Number of images | 9,648 | 15 | | Number of classes | 8 | 16 | | Crop and class | Soybean | 17 | | Organ of interest | | 18 | | Imaging environment | | 19 | | Resolution of image | | 20 | | Modality of optical sensors | | 21 | | Platform | | 22 | | Annotation strategy | | 23 | | Image variation | | 24 | | Extra description | | 25 | 26 | 27 | Classes and number of images 28 | ``` 29 | 485 bacterial_blight 30 | 1599 cercospora_leaf_blight 31 | 653 downey_mildew 32 | 1541 frogeye 33 | 1633 healthy 34 | 1035 potassium_deficiency 35 | 1628 soybean_rust 36 | 1082 target_spot 37 | 115 unused_cercospora_leaf_blight 38 | 119 unused_healthy 39 | 156 unused_soybean_rust 40 | ``` 41 | 42 | Examples of images and annotations if possible. 43 | -------------------------------------------------------------------------------- /data/ATLDSD.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:-----------------------------------------------------------------------------------------------| 4 | | Dataset name | ATLDSD | 5 | | Publication link | | 6 | | Dataset download link | [Science Data Bank](https://www.scidb.cn/en/detail?dataSetId=0e1f57004db842f99668d82183afd578) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | image classification, semantic segmentation | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 1,641 | 14 | | Number of classes | 5 | 15 | | Crop and class | | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | Controlled background | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/Apple2020.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | | Key | Value | 4 | |:----------------------------|:-----------------------------------------------------------------------------------| 5 | | Dataset name | Apple2020 | 6 | | Publication link | [Paper](https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.11390) | 7 | | Dataset download link | [Dataset](https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/overview) | 8 | | Year | 2020 | 9 | | Imaging location | | 10 | | Agricultural task | plant disease | 11 | | Machine learning task | image classification | 12 | | Machine learning challenge | | 13 | | Metric and performance | AUC-ROC: 0.984 | 14 | | Number of images | 3,642, 1,821 as official training with labels | 15 | | Number of classes | 4 | 16 | | Crop and class | Apple: health, rust, scab, multiple | 17 | | Organ of interest | Leaf | 18 | | Imaging environment | Field | 19 | | Resolution of image | | 20 | | Modality of optical sensors | | 21 | | Platform | | 22 | | Annotation strategy | | 23 | | Image variation | | 24 | | Extra description | | 25 | 26 | 27 | ### Classes and images 28 | ```angular2html 29 | 516 healthy 30 | 91 multiple_diseases 31 | 622 rust 32 | 592 scab 33 | ``` 34 | 35 | 36 | Examples of images and annotations if possible. 37 | ![Apple2020.png](Apple2020.png) -------------------------------------------------------------------------------- /data/Apple2020.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xml94/PPDRD/f7cab1812101f78fdd9894e183022527627a2c22/data/Apple2020.png -------------------------------------------------------------------------------- /data/Apple2021.md: -------------------------------------------------------------------------------- 1 | # Template to describe datasets 2 | 3 | | Key | Value | 4 | |:----------------------------|:-------------------------------------------------------------------------------| 5 | | Dataset name | Apple2021![](https://img.shields.io/badge/-Public-008000) | 6 | | Publication link | No | 7 | | Dataset download link | [Dataset](https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8/data) | 8 | | Year | 2021 | 9 | | Imaging location | | 10 | | Agricultural task | plant disease | 11 | | Machine learning task | image classification | 12 | | Machine learning challenge | | 13 | | Metric and performance | F1 0.883 in private test dataset | 14 | | Number of images | 18,632 | 15 | | Number of classes | 6 | 16 | | Crop and class | Apple: frog_eye_leaf_spot, healthy, powdery_mildew, rust, scab | 17 | | Organ of interest | | 18 | | Imaging environment | | 19 | | Resolution of image | | 20 | | Modality of optical sensors | | 21 | | Platform | | 22 | | Annotation strategy | | 23 | | Image variation | | 24 | | Extra description | | 25 | 26 | ### Classes and images 27 | ```angular2html 28 | 2957 complex 29 | 3181 frog_eye_leaf_spot 30 | 4624 healthy 31 | 1184 powdery_mildew 32 | 1860 rust 33 | 4826 scab 34 | ``` 35 | 36 | Examples of images and annotations if possible. 37 | -------------------------------------------------------------------------------- /data/BRACOL.md: -------------------------------------------------------------------------------- 1 | | Key | Value | 2 | |:----------------------------|:---------------------------------------------------------------------------------------------------------------------------| 3 | | Dataset name | BRACOL | 4 | | Publication link | [Computers and Electronics in Agriculture](https://www.sciencedirect.com/science/article/pii/S0168169919313225?via%3Dihub) | 5 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/yy2k5y8mxg/1) | 6 | | Year | 2020 | 7 | | Imaging location | | 8 | | Agricultural task | | 9 | | Machine learning task | image classification and semantic segmentation | 10 | | Machine learning challenge | | 11 | | Metric and performance | Acc of leaf: 0.956, Acc of symptom: 0.971 | 12 | | Number of images | leaf: 1,747 and local symptom: 2,209 | 13 | | Number of classes | 5 | 14 | | Crop and class | coffee | 15 | | Organ of interest | leaf | 16 | | Imaging environment | | 17 | | Resolution of image | | 18 | | Modality of optical sensors | | 19 | | Platform | | 20 | | Annotation strategy | | 21 | | Image variation | | 22 | | Extra description | | 23 | 24 | 25 | Examples of images and annotations if possible. 26 | -------------------------------------------------------------------------------- /data/BisqueCorn.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| 4 | | Dataset name | BisqueCorn | 5 | | Publication link | | 6 | | Dataset download link | [Healthy](https://bisque.cyverse.org/client_service/view?resource=https://bisque.cyverse.org/data_service/00-fsRrwb8afr4Q4diBdiWtF9)
[Diseased](https://bisque.cyverse.org/client_service/view?resource=https://bisque.cyverse.org/data_service/00-RwgzE6c8Mt3VKEZHitpe25) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | N.A | 13 | | Number of images | 1,785 | 14 | | Number of classes | 2 | 15 | | Crop and class | Corn leaf | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/CCMT.md: -------------------------------------------------------------------------------- 1 | | Key of metadata | Value | 2 | |:----------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| 3 | | Dataset name | CCMT ![](https://img.shields.io/badge/-Public-008000) | 4 | | Publication link | [Journal: Data in Brief](https://www.sciencedirect.com/science/article/pii/S2352340923004250) | 5 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/bwh3zbpkpv/1) | 6 | | Released Year | 2023 | 7 | | Imaging location | Ghana, Nairobi, Kenya | 8 | | Agricultural task | Pest and Disease recognition | 9 | | Machine learning task | Image classification | 10 | | Machine learning challenge | Class imbalance | 11 | | Metric and performance | | 12 | | Number of images | 24,881 | 13 | | Number of classes | 22 | 14 | | Crop and class | Cashew: anthracnose, gummosis, healthy, leaf miner, red rust.
Cassava: bacterial blight, brown spot, green mite, healthy, mosaic.
Maize: fall armyworm, grasshopper, healthy, leaf beetle, leaf blight, leaf spot, streak virus.
Tomato: healthy, leaf blight, leaf curl, septoria leaf spot, verticillium wilt. | 15 | | Organ of interest | leaves, fruits, pests, barks | 16 | | Imaging environment | Most of the images are in simple background | 17 | | Resolution of image | (400 × 400), (487 × 1080), (1080 × 518), (3024 × 4032), and (4032 × 3024) | 18 | | Modality of optical sensors | RGB JPEG | 19 | | Platform | Handled camera | 20 | | Annotation strategy | | 21 | | Image variation | | 22 | | Extra description | | 23 | 24 | 25 | 26 | ### Classes and images 27 | ```angular2html 28 | 6549 : ./Cashew 29 | 1729 : ./Cashew/anthracnose 30 | 392 : ./Cashew/gumosis 31 | 1368 : ./Cashew/healthy 32 | 1378 : ./Cashew/leaf miner 33 | 1682 : ./Cashew/red rust 34 | 7508 : ./Cassava 35 | 2614 : ./Cassava/bacterial blight 36 | 1481 : ./Cassava/brown spot 37 | 1015 : ./Cassava/green mite 38 | 1193 : ./Cassava/healthy 39 | 1205 : ./Cassava/mosaic 40 | 5358 : ./Maize 41 | 285 : ./Maize/fall armyworm 42 | 673 : ./Maize/grasshoper 43 | 208 : ./Maize/healthy 44 | 948 : ./Maize/leaf beetle 45 | 1006 : ./Maize/leaf blight 46 | 1259 : ./Maize/leaf spot 47 | 979 : ./Maize/streak virus 48 | 5805 : ./Tomato 49 | 470 : ./Tomato/healthy 50 | 1301 : ./Tomato/leaf blight 51 | 518 : ./Tomato/leaf curl 52 | 2743 : ./Tomato/septoria leaf spot 53 | 773 : ./Tomato/verticulium wilt 54 | ``` 55 | 56 | Examples of images and annotations if possible. 57 | -------------------------------------------------------------------------------- /data/CDRD.md: -------------------------------------------------------------------------------- 1 | # Template to describe datasets 2 | 3 | | Key | Value | 4 | |:----------------------------|:----------------------------------------------------------------------------------------------| 5 | | Dataset name | CDRD: Cucumber Disease Recognition Dataset | 6 | | Publication link | [Journal: Data in Brief](https://www.sciencedirect.com/science/article/pii/S2352340923004389) | 7 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/y6d3z6f8z9/1) | 8 | | Imaging location | Basundia, Jashore | 9 | | Agricultural task | disease recognition for leaves and fruits | 10 | | Machine learning task | Image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 1,289 | 14 | | Number of classes | 8 | 15 | | Crop and class | | 16 | | Organ of interest | leaf, fruit | 17 | | Imaging environment | Real field | 18 | | Resolution of image | 512*512 | 19 | | Modality of optical sensors | RGB camera | 20 | | Platform | Handled camera | 21 | | Annotation strategy | | 22 | | Image variation | disease severity, scale | 23 | | Extra description | same identity with different perspective | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | 28 | Anthracnose, 29 | Bacterial Wilt, 30 | Belly Rot, 31 | Downy Mildew, 32 | Pythium Fruit Rot, 33 | Gummy Stem Blight, 34 | Fresh leaves, 35 | Fresh cucumber. -------------------------------------------------------------------------------- /data/CLDCAmanda.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------------------------------------------| 4 | | Dataset name | CLDCAmanda | 5 | | Publication link | [Frontiers in Plant Science](https://www.frontiersin.org/articles/10.3389/fpls.2017.01852/full) | 6 | | Dataset download link | [Dataset link](https://scholarsphere.psu.edu/resources/215d1acd-2c1e-440b-a27a-03d212761ef7) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | Acc: 0.930 | 13 | | Number of images | 2,249 images are public with labels | 14 | | Number of classes | 6 | 15 | | Crop and class | Cassava leaf | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. -------------------------------------------------------------------------------- /data/CLDCMakerere.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------| 4 | | Dataset name | CLDCMakerere | 5 | | Publication link | No | 6 | | Dataset download link | [Official Kaggle challenge](https://www.kaggle.com/competitions/cassava-leaf-disease-classification) | 7 | | Year | 2020 | 8 | | Imaging location | | 9 | | Agricultural task | Plant disease recognition | 10 | | Machine learning task | Image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | Acc in test private: 0.913 | 13 | | Number of images | 21,397 | 14 | | Number of classes | 5 | 15 | | Crop and class | Cassava | 16 | | Organ of interest | Leaf | 17 | | Imaging environment | Field | 18 | | Resolution of image | | 19 | | Modality of optical sensors | RGB | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | [This link](https://www.kaggle.com/datasets/gauravduttakiit/cassava-leaf-disease-classification) seems the same images but with real name labels | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | 28 | 29 | 30 | -------------------------------------------------------------------------------- /data/CLDD.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------| 4 | | Dataset name | CLDCMakerere | 5 | | Publication link | | 6 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/3832tx2cb2/1) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | disease classification | 10 | | Machine learning task | image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 228 | 14 | | Number of classes | 3 | 15 | | Crop and class | Cassava leaf | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | 512*512 | 19 | | Modality of optical sensors | RGB | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | Controlled background | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/CitrusRauf.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------| 4 | | Dataset name | CitrusRauf | 5 | | Publication link | | 6 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/3f83gxmv57/2) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 609 as leaf and 150 as fruit | 14 | | Number of classes | 5 classes for both leaf and fruit | 15 | | Crop and class | Citrus leaf and fruit | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | Controlled background | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/CornNLB.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:---------------------------------------------------------------------------------------------| 4 | | Dataset name | CornNLB18222 | 5 | | Publication link | [Conference paper](https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-018-3548-6) | 6 | | Dataset download link | [Dataset](https://osf.io/p67rz/) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | disease localization | 10 | | Machine learning task | object detection | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 18,222 in total: 1,787 handled, 8,766 boom, 7,669 UAV | 14 | | Number of classes | 1 | 15 | | Crop and class | Maize leaf | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | three types of platforms: handled, boom, UAV. | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/CucumberNegm.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:-------------------------------------------------------------------------------------| 4 | | Dataset name | CucumberNegm | 5 | | Publication link | | 6 | | Dataset download link | [Kaggle](https://www.kaggle.com/datasets/kareem3egm/cucumber-plant-diseases-dataset) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | disease classification | 10 | | Machine learning task | image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 691 | 14 | | Number of classes | 2 | 15 | | Crop and class | cucumber leaf | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/DhanShomadhan.md: -------------------------------------------------------------------------------- 1 | | Key | Value | 2 | |:----------------------------|:----------------------------------------------------------------| 3 | | Dataset name | DhanShomadhan ![](https://img.shields.io/badge/-Public-008000) | 4 | | Publication link | [Arxiv](https://arxiv.org/ftp/arxiv/papers/2309/2309.07515.pdf) | 5 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/znsxdctwtt/1) | 6 | | Year | 2021 April 6 | 7 | | Imaging location | | 8 | | Agricultural task | Rice disease recognition | 9 | | Machine learning task | Image classification | 10 | | Machine learning challenge | domain shift | 11 | | Metric and performance | | 12 | | Number of images | 1,106 | 13 | | Number of classes | 5 | 14 | | Crop and class | Rice | 15 | | Organ of interest | Leaf | 16 | | Imaging environment | field and white background with labels | 17 | | Resolution of image | (1952, 4160) or (4160, 1952) | 18 | | Modality of optical sensors | | 19 | | Platform | | 20 | | Annotation strategy | | 21 | | Image variation | | 22 | | Extra description | | 23 | 24 | 25 | 26 | ### Classes and number of images 27 | ```angular2html 28 | ├── Field Background 337 29 | ├── Brown Spot 49 30 | ├── Leaf Scaled 4 31 | ├── Rice Blast 74 32 | ├── Rice Turgro 76 33 | └── Sheath Blight 64 34 | └── White Background 769 35 | ├── Brown Spot 90 36 | ├── Leaf Scaled 143 37 | ├── Rice Blast 198 38 | ├── Rice Tungro 119 39 | └── Shath Blight 219 40 | ``` 41 | 42 | 43 | Examples of images and annotations if possible. 44 | -------------------------------------------------------------------------------- /data/FieldPV.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:---------------------------------------------------------------------------------------------------------------| 4 | | Dataset name | FieldPV | 5 | | Publication link | [Computer and Electronics in Agriculture](https://www.sciencedirect.com/science/article/pii/S0168169921005408) | 6 | | Dataset download link | [GitHub](https://github.com/PatrickGui/FPDR/tree/master#experimental-data) | 7 | | Year | 2021 | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | Acc: 0.720 (training in PlantVillage) | 13 | | Number of images | 665 | 14 | | Number of classes | 38 | 15 | | Crop and class | 14 | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | Images and labels are collected from the Internet | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/FieldPlant.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------------------------------------------------------------------------------------| 4 | | Dataset name | FieldPlant | 5 | | Publication link | [IEEE ACCESS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086516) | 6 | | Dataset download link | [RobotFlow](https://universe.roboflow.com/plant-disease-detection/fieldplant) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | Localization | 10 | | Machine learning task | Object detection | 11 | | Machine learning challenge | | 12 | | Metric and performance | mAP: 0.144 | 13 | | Number of images | 5,156 | 14 | | Number of classes | 31 | 15 | | Crop and class | Corn, cassava, and tomato | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | very big such as 3000*4000 | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | Images may not be suitable for object detection because bounding boxes occupied almost whole image such as class Cassava_Brown_Leaf_Spot. | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | Some annotated objects are blurring. -------------------------------------------------------------------------------- /data/GFLDRauf.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | | Key | Value | 4 | |:----------------------------|:------------------------------------------------------------| 5 | | Dataset name | GFLDRauf | 6 | | Publication link | | 7 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/s8x6jn5cvr/1) | 8 | | Year | | 9 | | Imaging location | | 10 | | Agricultural task | | 11 | | Machine learning task | image classification | 12 | | Machine learning challenge | | 13 | | Metric and performance | N.A | 14 | | Number of images | 306 | 15 | | Number of classes | 4 | 16 | | Crop and class | Guava leaf and fruit | 17 | | Organ of interest | | 18 | | Imaging environment | | 19 | | Resolution of image | | 20 | | Modality of optical sensors | | 21 | | Platform | | 22 | | Annotation strategy | | 23 | | Image variation | | 24 | | Extra description | | 25 | 26 | 27 | Examples of images and annotations if possible. 28 | -------------------------------------------------------------------------------- /data/GLFD.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------------------------------------------| 4 | | Dataset name | GLFD (Guava Leaves and Fruits Dataset) | 5 | | Publication link | [Data in Brief](https://www.sciencedirect.com/science/article/pii/S235234092200378X?via%3Dihub) | 6 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/x84p2g3k6z/1) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | disease classification | 10 | | Machine learning task | image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 527 | 14 | | Number of classes | 5 | 15 | | Crop and class | Guava leaf and fruit | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | symptoms in very late stage | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/GrapevineDiseaseMalo.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------| 4 | | Dataset name | GrapevineDiseaseMalo | 5 | | Publication link | | 6 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/3dr9r3w3jn/2) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | localization and segmentation | 10 | | Machine learning task | object detection, segmentation | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 744 images for obj, 128 images for seg | 14 | | Number of classes | 3 for obj, 4 for seg | 15 | | Crop and class | Grape | 16 | | Organ of interest | leaf, shoot and bunch | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | 5 varieties of grape | 23 | | Extra description | Have constant luminance using industry flash. | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/GroundNutLeaf.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:-------------------------------------------------------------------------------------| 4 | | Dataset name | GroundNutLeaf | 5 | | Publication link | [Data in Brief](https://www.sciencedirect.com/science/article/pii/S2352340923003049) | 6 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/22p2vcbxfk/3) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | disease classification | 10 | | Machine learning task | image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 3,058 | 14 | | Number of classes | 5 | 15 | | Crop and class | Ground nut | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/HuyDoRice.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | | Key | Value | 4 | |:----------------------------|:----------------------------------------------------------------------------------| 5 | | Dataset name | HuyDoRice | 6 | | Publication link | | 7 | | Dataset download link | [Kaggle](https://www.kaggle.com/datasets/minhhuy2810/rice-diseases-image-dataset) | 8 | | Year | 2019 | 9 | | Imaging location | | 10 | | Agricultural task | | 11 | | Machine learning task | | 12 | | Machine learning challenge | | 13 | | Metric and performance | | 14 | | Number of images | 3,355 | 15 | | Number of classes | 4 | 16 | | Crop and class | Rice leaf | 17 | | Organ of interest | | 18 | | Imaging environment | | 19 | | Resolution of image | | 20 | | Modality of optical sensors | | 21 | | Platform | | 22 | | Annotation strategy | | 23 | | Image variation | | 24 | | Extra description | Controlled background | 25 | 26 | 27 | Examples of images and annotations if possible. 28 | -------------------------------------------------------------------------------- /data/IDADP.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:----------------------------------------------------------------------------------| 4 | | Dataset name | IDADP | 5 | | Publication link | | 6 | | Dataset download link | [Link](https://www.scidb.cn/en/detail?dataSetId=76b39c9c435d4035b5076412c2ddcb61) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 3,596 | 14 | | Number of classes | 7 | 15 | | Crop and class | Grape | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/MaizeCraze.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:--------------------------------------------------------------------------------------------------------| 4 | | Dataset name | MaizeCraze | 5 | | Publication link | [Same author paper](https://www.mdpi.com/2223-7747/11/15/1942) | 6 | | Dataset download link | [Datasets link](https://researchdata.up.ac.za/articles/dataset/Diseases_of_maize_in_the_field/20237613) | 7 | | Year | 2022 | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 2,355 | 14 | | Number of classes | 6 + unknown + other | 15 | | Crop and class | Maize | 16 | | Organ of interest | leaf | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/NZDLPlantDiseaseV1.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:---------------------------------------------------------------------| 4 | | Dataset name | NZDLPlantDiseaseV1 | 5 | | Publication link | [IEEE ACCESS](https://ieeexplore.ieee.org/abstract/document/9864587) | 6 | | Dataset download link | [Github](https://github.com/kmarif/NZDLPlantDisease-v1) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | Object detection | 11 | | Machine learning challenge | | 12 | | Metric and performance | mAP: 0.745 | 13 | | Number of images | 3,337 | 14 | | Number of classes | 20 | 15 | | Crop and class | 5 crops | 16 | | Organ of interest | Leaf, stem, fruit | 17 | | Imaging environment | | 18 | | Resolution of image | 256*256 | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/NZDLPlantDiseaseV2.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:--------------------------------------------------------------------------------------------------| 4 | | Dataset name | NZDLPlantDiseaseV2 | 5 | | Publication link | [Frontiers in Plant Science](https://www.frontiersin.org/articles/10.3389/fpls.2022.1008079/full) | 6 | | Dataset download link | [Github](https://github.com/kmarif/NZDLPlantDisease-v2) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 3,039 | 14 | | Number of classes | 28 | 15 | | Crop and class | 8 vegetables | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | 256*256 | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/PCApple2023.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------| 4 | | Dataset name | PCApple2023 | 5 | | Publication link | | 6 | | Dataset download link | [aistudio](https://aistudio.baidu.com/datasetdetail/215559) | 7 | | Year | 2023 | 8 | | Imaging location | | 9 | | Agricultural task | disease classification | 10 | | Machine learning task | image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 10,212 labeled in training, 4,371 unlabeled in test | 14 | | Number of classes | 9 | 15 | | Crop and class | Apple leaf | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | controlled background yet different | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/PDD271.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------------| 4 | | Dataset name | PDD271 | 5 | | Publication link | [IEEE Trans on TIP](https://ieeexplore.ieee.org/document/9325065) | 6 | | Dataset download link | [Github](https://github.com/liuxindazz/PDD271) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | Disease classification | 10 | | Machine learning task | Image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | Acc: 0.855 | 13 | | Number of images | 2,710 = 271 * 10 | 14 | | Number of classes | 271 | 15 | | Crop and class | | 16 | | Organ of interest | Leaf | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/PDDM.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:-----------------------------------------------------| 4 | | Dataset name | PDDM plant disease diagnosis multimodal | 5 | | Publication link | | 6 | | Dataset download link | [Part dataset](http://pd.dm.samlab.cn/download.html) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 205,007 | 14 | | Number of classes | 81 diseases + 35 healthy | 15 | | Crop and class | 40 crops | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | images and text description | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/PaddyDoctor.md: -------------------------------------------------------------------------------- 1 | # Template to describe datasets 2 | 3 | | Key | Value | 4 | |:-------------------------------------|:-----------------------------------------------------------------------------------------------| 5 | | Dataset name | PaddyDoctor ![](https://img.shields.io/badge/-Public-008000) | 6 | | Publication link | [Paper](https://arxiv.org/abs/2205.11108) | 7 | | Dataset download link | [Dataset in Kaggle](https://www.kaggle.com/competitions/paddy-disease-classification/overview) | 8 | | Imaging location | | 9 | | Agricultural task | rice disease recognition | 10 | | Machine learning task | image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance, architecture | Acc 99.0% | 13 | | Number of images | training: 10,407 test: 3,469 | 14 | | Number of classes | 10 | 15 | | Crop and class | Rice leaf | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | (1080, 1440) | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | ![paddy_doctor.png](paddy_doctor.png) -------------------------------------------------------------------------------- /data/PlantConservation.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | | Key | Value | 4 | |:----------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------| 5 | | Dataset name | PlantConservation ![](https://img.shields.io/badge/-Public-008000) | 6 | | Publication link | [Conference paper](https://ieeexplore.ieee.org/document/9036158) | 7 | | Dataset download link | [Mendeley ](https://data.mendeley.com/datasets/hb74ynkjcn/5) | 8 | | Year | Collected in 2019 March to May | 9 | | Imaging location | Shri Mata Vaishno Devi University, India. | 10 | | Agricultural task | Recognition of plant disease and species | 11 | | Machine learning task | Image classification | 12 | | Machine learning challenge | | 13 | | Metric and performance | | 14 | | Number of images | 4,503 | 15 | | Number of classes | 22 in total = 11 healthy and 11 diseased, every plant has only one diseased images. | 16 | | Crop and class | 12 crops, Mango, Arjun, Alstonia Scholaris, Guava, Bael,
Jamun, Jatropha, Pongamia Pinnata,
Basil, Pomegranate, Lemon, and Chinar | 17 | | Organ of interest | Leaf | 18 | | Imaging environment | Laboratory | 19 | | Resolution of image | (6000, 4000) | 20 | | Modality of optical sensors | JPEG | 21 | | Platform | A fixed Nikon D5300 camera | 22 | | Annotation strategy | | 23 | | Image variation | Leaf shape, size, color because of leaf life cycle: young, mature, older | 24 | | Extra description | Diseased and healthy | 25 | 26 | 27 | ### Classes and number of images 28 | ```angular2html 29 | 4502 : . 30 | 433 : ./Alstonia Scholaris (P2) 31 | 254 : ./Alstonia Scholaris (P2)/diseased 32 | 179 : ./Alstonia Scholaris (P2)/healthy 33 | 452 : ./Arjun (P1) 34 | 232 : ./Arjun (P1)/diseased 35 | 220 : ./Arjun (P1)/healthy 36 | 118 : ./Bael (P4) 37 | 118 : ./Bael (P4)/diseased 38 | 148 : ./Basil (P8) 39 | 148 : ./Basil (P8)/healthy 40 | 223 : ./Chinar (P11) 41 | 120 : ./Chinar (P11)/diseased 42 | 103 : ./Chinar (P11)/healthy 43 | 419 : ./Gauva (P3) 44 | 142 : ./Gauva (P3)/diseased 45 | 277 : ./Gauva (P3)/healthy 46 | 624 : ./Jamun (P5) 47 | 345 : ./Jamun (P5)/diseased 48 | 279 : ./Jamun (P5)/healthy 49 | 257 : ./Jatropha (P6) 50 | 124 : ./Jatropha (P6)/diseased 51 | 133 : ./Jatropha (P6)/healthy 52 | 236 : ./Lemon (P10) 53 | 77 : ./Lemon (P10)/diseased 54 | 159 : ./Lemon (P10)/healthy 55 | 435 : ./Mango (P0) 56 | 265 : ./Mango (P0)/diseased 57 | 170 : ./Mango (P0)/healthy 58 | 559 : ./Pomegranate (P9) 59 | 272 : ./Pomegranate (P9)/diseased 60 | 287 : ./Pomegranate (P9)/healthy 61 | 598 : ./Pongamia Pinnata (P7) 62 | 276 : ./Pongamia Pinnata (P7)/diseased 63 | 322 : ./Pongamia Pinnata (P7)/healthy 64 | ``` 65 | 66 | ### Disk memory 67 | ```angular2html 68 | 900.8 MiB [######## ] 600 /Pongamia Pinnata (P7) 69 | 818.7 MiB [######## ] 561 /Pomegranate (P9) 70 | 702.8 MiB [###### ] 435 /Alstonia Scholaris (P2) 71 | 699.7 MiB [###### ] 437 /Mango (P0) 72 | 686.3 MiB [###### ] 421 /Gauva (P3) 73 | 674.8 MiB [###### ] 454 /Arjun (P1) 74 | 403.7 MiB [#### ] 259 /Jatropha (P6) 75 | 355.8 MiB [### ] 238 /Lemon (P10) 76 | 353.2 MiB [### ] 225 /Chinar (P11) 77 | 224.4 MiB [## ] 149 /Basil (P8) 78 | 156.3 MiB [# ] 119 /Bael (P4) 79 | ``` 80 | Examples of images and annotations if possible. 81 | -------------------------------------------------------------------------------- /data/PlantDoc.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:---------------------------------------------------------------------------| 4 | | Dataset name | PlantDoc | 5 | | Publication link | [ACM conference paper](https://dl.acm.org/doi/pdf/10.1145/3371158.3371196) | 6 | | Dataset download link | [Github](https://github.com/pratikkayal/PlantDoc-Dataset) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | Disease classification and localization | 10 | | Machine learning task | classification and object detection | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 2,598 | 14 | | Number of classes | 27 | 15 | | Crop and class | | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | images are from the Internet and labels are from human | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/PlantVillage.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:----------------------------------------------------------------------------------------| 4 | | Dataset name | PlantVillage | 5 | | Publication link | [Arxiv](https://arxiv.org/abs/1511.08060) | 6 | | Dataset download link | [Not official](https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 55,305 | 14 | | Number of classes | 38 | 15 | | Crop and class | 14 crops | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/Rice1426.md: -------------------------------------------------------------------------------- 1 | # Template to describe datasets 2 | 3 | | Key | Value | 4 | |:----------------------------|:---------------------------------------------------------------------------------------------| 5 | | Dataset name | Rice1426 | 6 | | Publication link | [Paper](https://www.sciencedirect.com/science/article/pii/S1537511020300830?via%3Dihub#sec2) | 7 | | Dataset download link | [Dataset](https://drive.google.com/drive/folders/1ewBesJcguriVTX8sRJseCDbXAF_T4akK) | 8 | | Year | | 9 | | Imaging location | | 10 | | Agricultural task | | 11 | | Machine learning task | | 12 | | Machine learning challenge | | 13 | | Metric and performance | Acc: 0.971 | 14 | | Number of images | 1,426 | 15 | | Number of classes | 9 | 16 | | Crop and class | [Rice](https://github.com/xml94/PRD/blob/main/classes_name.md#rice1426) | 17 | | Organ of interest | | 18 | | Imaging environment | | 19 | | Resolution of image | | 20 | | Modality of optical sensors | | 21 | | Platform | | 22 | | Annotation strategy | | 23 | | Image variation | | 24 | | Extra description | | 25 | 26 | 27 | Examples of images and annotations if possible. 28 | -------------------------------------------------------------------------------- /data/Rice5932.md: -------------------------------------------------------------------------------- 1 | # Template to describe datasets 2 | 3 | | Key | Value | 4 | |:----------------------------|:-----------------------------------------------------------------------------------| 5 | | Dataset name | Rice5932 | 6 | | Publication link | [Paper](https://www.sciencedirect.com/science/article/pii/S0168169919326997#s0010) | 7 | | Dataset download link | [Dataset](https://data.mendeley.com/datasets/fwcj7stb8r/1) | 8 | | Year | | 9 | | Imaging location | | 10 | | Agricultural task | | 11 | | Machine learning task | | 12 | | Machine learning challenge | | 13 | | Metric and performance | Acc: 0.984 | 14 | | Number of images | 5,932 | 15 | | Number of classes | 4 | 16 | | Crop and class | Rice: Bacterial Leaf Blight, Brown spot, Blast, Tungro | 17 | | Organ of interest | | 18 | | Imaging environment | | 19 | | Resolution of image | | 20 | | Modality of optical sensors | | 21 | | Platform | | 22 | | Annotation strategy | | 23 | | Image variation | | 24 | | Extra description | | 25 | 26 | 27 | Examples of images and annotations if possible. 28 | -------------------------------------------------------------------------------- /data/RoCoLe.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------------------------------------------| 4 | | Dataset name | RoCoLe | 5 | | Publication link | [Data in Brief](https://www.sciencedirect.com/science/article/pii/S2352340919307693?via%3Dihub) | 6 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/c5yvn32dzg/2) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | disease classification, segmentation | 10 | | Machine learning task | image classification and segmentation | 11 | | Machine learning challenge | | 12 | | Metric and performance | N.A | 13 | | Number of images | 1,560 | 14 | | Number of classes | seg: 2, cls: 6 | 15 | | Crop and class | Coffee leaf | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | with severity levels. Two diseases and one disease has 4 level. | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/SoybeanMignoni.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------------------------------------------| 4 | | Dataset name | SoybeanMignoni | 5 | | Publication link | [Data in Brief](https://www.sciencedirect.com/science/article/pii/S2352340921010313?via%3Dihub) | 6 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/bycbh73438/1) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | visual pattern attacked by pests | 10 | | Machine learning task | image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 6,410 | 14 | | Number of classes | 3 | 15 | | Crop and class | Soybean leaf | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/TaiwanTomato.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------| 4 | | Dataset name | TaiwanTomato | 5 | | Publication link | | 6 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/ngdgg79rzb/1) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | disease classification | 10 | | Machine learning task | image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | N.A | 13 | | Number of images | 622 | 14 | | Number of classes | 6 | 15 | | Crop and class | Tomato | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | collected from the Internet, local pattern | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/WheatLeafDataset.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------------------------------------------------------------| 4 | | Dataset name | WheatLeafDataset | 5 | | Publication link | | 6 | | Dataset download link | [Mendeley](https://data.mendeley.com/datasets/wgd66f8n6h/1) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | N.A | 13 | | Number of images | 407 | 14 | | Number of classes | 3 | 15 | | Crop and class | Wheat | 16 | | Organ of interest | Leaf | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | Controlled background | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/WheatLong.md: -------------------------------------------------------------------------------- 1 | | Key of metadata | Value | 2 | |:----------------------------|:----------------------------------------------------------------------------------------------------| 3 | | Dataset name | WheatLong ![](https://img.shields.io/badge/-Partial--public-90EE90) | 4 | | Publication link | [Journal: Plant Pathology](https://bsppjournals.onlinelibrary.wiley.com/doi/full/10.1111/ppa.13684) | 5 | | Dataset download link | [Dataset](https://www.ebi.ac.uk/biostudies/BioImages/studies/S-BIAD682?query=Septoria) | 6 | | Released Year | 2022 | 7 | | Imaging location | UK and Ireland in the summer of 2019 | 8 | | Agricultural task | Disease recognition, compare human expert and deep learning | 9 | | Machine learning task | Image classification | 10 | | Machine learning challenge | Class bias such as black pot for mildew in the breeding trial | 11 | | Metric and performance | Acc: 0.971 | 12 | | Number of images | 999 with labels are avaiable, 19,160 in total | 13 | | Number of classes | 5 | 14 | | Crop and class | wheat: healthy, brown rust, mildew, septoria, yellow rust. | 15 | | Organ of interest | leaves, fruit | 16 | | Imaging environment | Field and breeding trial | 17 | | Resolution of image | (567, 756) | 18 | | Modality of optical sensors | RGB JPEG | 19 | | Platform | Handled camera | 20 | | Annotation strategy | | 21 | | Image variation | background, illumination, | 22 | | Extra description | similar scale | 23 | 24 | 25 | * 999 images are available [here](https://zenodo.org/record/7573133) 26 | * Holistic dataset is not available although it is public [here](https://www.ebi.ac.uk/biostudies/BioImages/studies/S-BIAD682?query=Septoria) 27 | 28 | Examples of images and annotations if possible. -------------------------------------------------------------------------------- /data/demo.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:------| 4 | | Dataset name | | 5 | | Publication link | | 6 | | Dataset download link | | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | | 14 | | Number of classes | | 15 | | Crop and class | | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/iBean.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:---------------------------------------------------| 4 | | Dataset name | iBean | 5 | | Publication link | | 6 | | Dataset download link | [GitHub](https://github.com/AI-Lab-Makerere/ibean) | 7 | | Year | | 8 | | Imaging location | | 9 | | Agricultural task | Disease classification | 10 | | Machine learning task | Image classification | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 1,296 | 14 | | Number of classes | 3 | 15 | | Crop and class | Bean | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/iCassava.md: -------------------------------------------------------------------------------- 1 | 2 | | Key | Value | 3 | |:----------------------------|:--------------------------------------------------------------| 4 | | Dataset name | iCassava | 5 | | Publication link | [Arxiv](https://arxiv.org/pdf/1908.02900.pdf) | 6 | | Dataset download link | [Kaggle](https://www.kaggle.com/competitions/cassava-disease) | 7 | | Year | 2019 | 8 | | Imaging location | | 9 | | Agricultural task | | 10 | | Machine learning task | | 11 | | Machine learning challenge | | 12 | | Metric and performance | | 13 | | Number of images | 5,656 with public labels, 3,774 without public labels | 14 | | Number of classes | 5 | 15 | | Crop and class | Cassava | 16 | | Organ of interest | | 17 | | Imaging environment | | 18 | | Resolution of image | | 19 | | Modality of optical sensors | | 20 | | Platform | | 21 | | Annotation strategy | | 22 | | Image variation | | 23 | | Extra description | | 24 | 25 | 26 | Examples of images and annotations if possible. 27 | -------------------------------------------------------------------------------- /data/paddy_doctor.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xml94/PPDRD/f7cab1812101f78fdd9894e183022527627a2c22/data/paddy_doctor.png --------------------------------------------------------------------------------