├── .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
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3 | /workspace.xml
4 | # Editor-based HTTP Client requests
5 | /httpRequests/
6 | # Datasource local storage ignored files
7 | /dataSources/
8 | /dataSources.local.xml
9 |
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/README.md:
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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 .
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 | ```
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/TBD/plantwild.md:
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1 | [plant wild github](https://tqwei05.github.io/PlantWild/)
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/data/ASDID.md:
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1 |
2 |
3 | | Key | Value |
4 | |:----------------------------|:------------------------------------------------------------------------------------------------------------------------------------|
5 | | Dataset name | ASDID |
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 |
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/data/ATLDSD.md:
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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 |
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/data/Apple2020.md:
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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 | 
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/data/Apple2020.png:
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https://raw.githubusercontent.com/xml94/PPDRD/f7cab1812101f78fdd9894e183022527627a2c22/data/Apple2020.png
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/data/Apple2021.md:
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1 | # Template to describe datasets
2 |
3 | | Key | Value |
4 | |:----------------------------|:-------------------------------------------------------------------------------|
5 | | Dataset name | Apple2021 |
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 |
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/data/BRACOL.md:
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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 |
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/data/BisqueCorn.md:
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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 |
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/data/CCMT.md:
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1 | | Key of metadata | Value |
2 | |:----------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
3 | | Dataset name | CCMT  |
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 |
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/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 |
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/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 |
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/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 |
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/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  |
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 |
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/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 |
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/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 |
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/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 |
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/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 |
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/data/GroundNutLeaf.md:
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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 |
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/data/HuyDoRice.md:
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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 |
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/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 |
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/data/MaizeCraze.md:
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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 |
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/data/NZDLPlantDiseaseV1.md:
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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 |
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/data/NZDLPlantDiseaseV2.md:
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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 |
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/data/PCApple2023.md:
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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 |
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/data/PDD271.md:
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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 |
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/data/PDDM.md:
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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 |
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/data/PaddyDoctor.md:
--------------------------------------------------------------------------------
1 | # Template to describe datasets
2 |
3 | | Key | Value |
4 | |:-------------------------------------|:-----------------------------------------------------------------------------------------------|
5 | | Dataset name | PaddyDoctor  |
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 | 
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/data/PlantConservation.md:
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1 |
2 |
3 | | Key | Value |
4 | |:----------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|
5 | | Dataset name | PlantConservation  |
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 |
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/data/PlantDoc.md:
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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 |
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/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 |
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/data/Rice1426.md:
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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 |
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/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 |
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/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 |
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/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 |
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/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 |
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/data/WheatLeafDataset.md:
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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 |
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/data/WheatLong.md:
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1 | | Key of metadata | Value |
2 | |:----------------------------|:----------------------------------------------------------------------------------------------------|
3 | | Dataset name | WheatLong  |
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.
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/data/demo.md:
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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 |
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/data/iBean.md:
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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 |
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/data/iCassava.md:
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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 |
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/data/paddy_doctor.png:
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https://raw.githubusercontent.com/xml94/PPDRD/f7cab1812101f78fdd9894e183022527627a2c22/data/paddy_doctor.png
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