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Merged
js2552 merged 1 commit into from
Jun 30, 2026
Merged

moving some datasets from s3 to hf #24

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147 changes: 0 additions & 147 deletions static/data/datasets.json
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Original file line number Diff line number Diff line change
Expand Up @@ -216,37 +216,6 @@
"apple"
]
},
{
"name": "arabica_coffee_leaf_disease_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "arabica_coffee_leaf_disease_classification",
"location": "Kenya, Africa",
"sensor_modality": "RGB",
"real_or_synthetic": "real",
"platform": "uav",
"input_data_format": "jpg, jpeg",
"annotation_format": "directory_names",
"num_images": 58549,
"documentation": "https://www.sciencedirect.com/science/article/pii/S2352340921004261?via%3Dihub#sec0001",
"classes": "Cerscospora, Healthy, Leaf_rust, Miner, Phoma",
"stats_mean": [
0.466,
0.613,
0.405
],
"stats_std": [
0.132,
0.101,
0.139
],
"examples_image_url": "/img/agml/sample_images/arabica_coffee_leaf_disease_classification_examples.webp",
"source": "agml",
"license": "CC BY 4.0",
"citation": "@article{JEPKOECH2021107142, title = {Arabica coffee leaf images dataset for coffee leaf disease detection and classification}, journal = {Data in Brief}, volume = {36}, pages = {107142}, year = {2021}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2021.107142}, url = {https://www.sciencedirect.com/science/article/pii/S2352340921004261}, author = {Jennifer Jepkoech and David Muchangi Mugo and Benson K. Kenduiywo and Edna Chebet Too}}",
"crop_types": [
"coffee"
]
},
{
"name": "autonomous_greenhouse_regression",
"machine_learning_task": null,
Expand All @@ -270,68 +239,6 @@
"lettuce"
]
},
{
"name": "banana_leaf_disease_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "banana_leaf_disease_classification",
"location": "Ethiopia, Africa",
"sensor_modality": "RGB",
"real_or_synthetic": "real",
"platform": "uav",
"input_data_format": "JPG",
"annotation_format": "directory_names",
"num_images": 1288,
"documentation": "https://www.researchgate.net/publication/380900090_Sigatoka_and_Xanthomonas_Banana_Leaf_Disease_Detection_Via_Transfer_Learning",
"classes": "healthy, segatoka, xamthomonas",
"stats_mean": [
0.44,
0.479,
0.23
],
"stats_std": [
0.205,
0.204,
0.186
],
"examples_image_url": "/img/agml/sample_images/banana_leaf_disease_classification_examples.webp",
"source": "agml",
"license": "CC BY 4.0",
"citation": "hailu, yordanos (2021), “Banana Leaf Disease Images”, Mendeley Data, V1, doi: 10.17632/rjykr62kdh.1",
"crop_types": [
"banana"
]
},
{
"name": "bean_disease_uganda",
"machine_learning_task": "image_classification",
"agricultural_task": "disease_classification",
"location": "Uganda, Africa",
"sensor_modality": "RGB",
"real_or_synthetic": "real",
"platform": "handheld",
"input_data_format": "JPG",
"annotation_format": "directory_names",
"num_images": 1295,
"documentation": "https://github.com/AI-Lab-Makerere/ibean/",
"classes": "angular_leaf_spot, bean_rust, healthy",
"stats_mean": [
0.485,
0.519,
0.311
],
"stats_std": [
0.182,
0.199,
0.169
],
"examples_image_url": "/img/agml/sample_images/bean_disease_uganda_examples.webp",
"source": "agml",
"license": "MIT",
"citation": "",
"crop_types": [
"bean"
]
},
{
"name": "bean_synthetic_earlygrowth_aerial",
"machine_learning_task": "semantic_segmentation",
Expand Down Expand Up @@ -386,29 +293,6 @@
"betel"
]
},
{
"name": "blackgram_plant_leaf_disease_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "blackgram_plant_leaf_disease_classification",
"location": "India, Asia",
"sensor_modality": "RGB",
"real_or_synthetic": "real",
"platform": "uav",
"input_data_format": "JPG",
"annotation_format": "directory_names",
"num_images": 1007,
"documentation": "https://www.sciencedirect.com/science/article/pii/S2352340922009295",
"classes": "anthracnose, healthy, leaf_crinckle, powdery_mildew, yellow_mosaic",
"stats_mean": null,
"stats_std": null,
"examples_image_url": "/img/agml/sample_images/blackgram_plant_leaf_disease_classification_examples.webp",
"source": "agml",
"license": "CC BY 4.0",
"citation": "Talasila, Srinivas; Rawal, Kirti; Sethi, Gaurav; MSS, Sanjay; M, Surya Prakash Reddy (2022), “Blackgram Plant Leaf Disease Dataset”, Mendeley Data, V3, doi: 10.17632/zfcv9fmrgv.3",
"crop_types": [
"blackgram"
]
},
{
"name": "carrot_weeds_germany",
"machine_learning_task": "semantic_segmentation",
Expand Down Expand Up @@ -440,37 +324,6 @@
"carrot"
]
},
{
"name": "chilli_leaf_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "chilli_leaf_classification",
"location": "India, Asia",
"sensor_modality": "RGB",
"real_or_synthetic": "real",
"platform": "uav",
"input_data_format": "JPG",
"annotation_format": "directory_names",
"num_images": 10974,
"documentation": "https://www.researchgate.net/publication/380611658_Dataset_of_Chilli_and_Onion_Plant_Leaf_Images_for_Classification_and_Detection",
"classes": "cercospora, healthy, mites_and_trips, nutritional, powdery_mildew",
"stats_mean": [
0.54,
0.562,
0.4
],
"stats_std": [
0.178,
0.177,
0.182
],
"examples_image_url": "/img/agml/sample_images/chilli_leaf_classification_examples.webp",
"source": "agml",
"license": "CC BY 4.0",
"citation": "Aishwarya, M.P & Reddy, A.. (2024). Dataset of Chilli and Onion Plant Leaf Images for Classification and Detection. Data in Brief. 54. 110524. 10.1016/j.dib.2024.110524. ",
"crop_types": [
"chilli"
]
},
{
"name": "coconut_tree_disease_classification",
"machine_learning_task": "image_classification",
Expand Down
204 changes: 204 additions & 0 deletions static/data/hf_datasets.json
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Expand Up @@ -2579,5 +2579,209 @@
"source": "huggingface",
"hf_link": "https://huggingface.co/datasets/Project-AgML/potato_leaf_blight_classification",
"examples_image_url": "/img/agml/sample_images/potato_leaf_blight_classification_sample.webp"
},
{
"name": "fruit_leaf_variety_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "variety_classification",
"location": "Bangladesh",
"environment": "lab",
"real_or_synthetic": "real",
"crop_types": [
"Aegle marmelos",
"Black plum",
"Custard Apple",
"Guava",
"Jackfruit",
"Lotkon",
"Lychee",
"Mango",
"Plum",
"Star Fruit"
],
"sensor_modality": "rgb",
"input_data_format": "image_folder",
"annotation_format": "classLabel",
"num_images": 3173,
"classes": [
"Aegle marmelos",
"Black plum",
"Custard Apple",
"Guava",
"Jackfruit",
"Lotkon",
"Lychee",
"Mango",
"Plum",
"Star Fruit"
],
"license": "cc-by-4.0",
"documentation": "https://doi.org/10.1016/j.dib.2025.111879",
"citation": "Abedin, Minhajul ; Islam, Md. Sujon ; Sultana, Dr. Naznin (2025), “Multi-Class Fruit Leaf Classification Dataset (10 Classes)”, Mendeley Data, V2, doi: 10.17632/4gxzx6h7gv.2",
"zip_size_bytes": 10452246264,
"source": "huggingface",
"hf_link": "https://huggingface.co/datasets/Project-AgML/fruit_leaf_variety_classification",
"examples_image_url": "/img/agml/sample_images/fruit_leaf_variety_classification_sample.png"
},
{
"name": "tropical_flower_variety_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "variety_classification",
"location": "Bangladesh",
"environment": "lab",
"real_or_synthetic": "real",
"crop_types": [
"Bougainvillea",
"Crown of thorns",
"Hibiscus",
"Jungle geranium",
"Madagascar periwinkle",
"Marigold",
"Rose"
],
"sensor_modality": "rgb",
"input_data_format": "image_folder",
"annotation_format": "classLabel",
"num_images": 4319,
"classes": [
"Bougainvillea",
"Crown of thorns",
"Hibiscus",
"Jungle geranium",
"Madagascar periwinkle",
"Marigold",
"Rose"
],
"license": "cc-by-4.0",
"documentation": "https://doi.org/10.1016/j.dib.2025.111374",
"citation": "Rahat, Riazul Islam; hossain, Md.Sohag; Mojumdar, Mayen Uddin ; Chakraborty, Narayan Ranjan; Noori, Sheak Rashed Haider; Siddiquee, Shah Md Tanvir (2024), “Tropical Flower Dataset: Seven Species from Bangladesh for Classification and Ecological Research.”, Mendeley Data, V1, doi: 10.17632/njfg9nh92t.1",
"zip_size_bytes": 1666250266,
"source": "huggingface",
"hf_link": "https://huggingface.co/datasets/Project-AgML/tropical_flower_variety_classification",
"examples_image_url": "/img/agml/sample_images/tropical_flower_variety_classification_sample.png"
},
{
"name": "arabica_coffee_leaf_disease_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "disease_classification",
"location": "Kenya, Africa",
"sensor_modality": "RGB",
"real_or_synthetic": "real",
"platform": "uav",
"environment": "field",
"input_data_format": "jpg, jpeg",
"annotation_format": "classLabel",
"num_images": 58549,
"documentation": "https://doi.org/10.1016/j.dib.2021.107142",
"classes": ["Cerscospora, Healthy, Leaf_rust, Miner, Phoma"],
"stats_mean": [
0.466,
0.613,
0.405
],
"stats_std": [
0.132,
0.101,
0.139
],
"examples_image_url": "/img/agml/sample_images/arabica_coffee_leaf_disease_classification_sample.png",
"source": "huggingface",
"license": "cc-by-4.0",
"citation": "Jepkoech, jennifer; Kenduiywo, Benson; Mugo, David; Chebet, Edna (2021), “JMuBEN”, Mendeley Data, V1, doi: 10.17632/t2r6rszp5c.1",
"crop_types": [
"coffee"
],
"hf_link": "https://huggingface.co/datasets/Project-AgML/arabica_coffee_leaf_disease_classification"
},
{
"name": "banana_leaf_disease_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "disease_classification",
"location": "Ethiopia, Africa",
"sensor_modality": "RGB",
"real_or_synthetic": "real",
"platform": "uav",
"environment": "field",
"input_data_format": "JPG",
"annotation_format": "classLabel",
"num_images": 1288,
"documentation": "https://www.researchgate.net/publication/380900090_Sigatoka_and_Xanthomonas_Banana_Leaf_Disease_Detection_Via_Transfer_Learning",
"classes": "healthy, segatoka, xamthomonas",
"stats_mean": [
0.44,
0.479,
0.23
],
"stats_std": [
0.205,
0.204,
0.186
],
"examples_image_url": "/img/agml/sample_images/banana_leaf_disease_classification_examples.webp",
"source": "agml",
"license": "CC BY 4.0",
"citation": "hailu, yordanos (2021), “Banana Leaf Disease Images”, Mendeley Data, V1, doi: 10.17632/rjykr62kdh.1",
"crop_types": [
"banana"
],
"hf_link": "https://huggingface.co/datasets/Project-AgML/banana_leaf_disease_classification"
},
{
"name": "bean_disease_uganda",
"machine_learning_task": "image_classification",
"agricultural_task": "disease_classification",
"location": "Uganda, Africa",
"sensor_modality": "RGB",
"real_or_synthetic": "real",
"platform": "handheld",
"environment": "Field",
"input_data_format": "JPG",
"annotation_format": "classLabel",
"num_images": 1295,
"documentation": "https://github.com/AI-Lab-Makerere/ibean/",
"classes": "angular_leaf_spot, bean_rust, healthy",
"stats_mean": [
0.485,
0.519,
0.311
],
"stats_std": [
0.182,
0.199,
0.169
],
"examples_image_url": "/img/agml/sample_images/bean_disease_uganda_examples.webp",
"source": "agml",
"license": "MIT",
"citation": "",
"crop_types": [
"bean"
],
"hf_link": "https://huggingface.co/datasets/Project-AgML/bean_disease_uganda"
},
{
"name": "blackgram_plant_leaf_disease_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "disease_classification",
"location": "India, Asia",
"sensor_modality": "RGB",
"real_or_synthetic": "real",
"platform": "uav",
"environment": "Field",
"input_data_format": "JPG",
"annotation_format": "classLabel",
"num_images": 1007,
"documentation": "https://doi.org/10.1016/j.dib.2022.108725",
"classes": ["anthracnose, healthy, leaf_crinckle, powdery_mildew, yellow_mosaic"],
"stats_mean": null,
"stats_std": null,
"examples_image_url": "/img/agml/sample_images/blackgram_plant_leaf_disease_classification_examples.webp",
"source": "agml",
"license": "CC BY 4.0",
"citation": "Talasila, Srinivas; Rawal, Kirti; Sethi, Gaurav; MSS, Sanjay; M, Surya Prakash Reddy (2022), “Blackgram Plant Leaf Disease Dataset”, Mendeley Data, V3, doi: 10.17632/zfcv9fmrgv.3",
"crop_types": [
"blackgram"
],
"hf_link": "https://huggingface.co/datasets/Project-AgML/blackgram_plant_leaf_disease_classification"
}
]
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