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js2552 merged 1 commit into from
Jun 28, 2026
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new datasets #22

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203 changes: 203 additions & 0 deletions static/data/hf_datasets.json
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Original file line number Diff line number Diff line change
Expand Up @@ -2376,5 +2376,208 @@
"source": "huggingface",
"hf_link": "https://huggingface.co/datasets/Project-AgML/sorghum_weed_segmentation",
"examples_image_url": "/img/agml/sample_images/sorghum_weed_segmentation_sample.webp"
},
{
"name": "AgriVision4_disease_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "disease_classification",
"location": "Bangladesh",
"environment": "lab",
"real_or_synthetic": "real",
"crop_types": [
"Bottle Gourd",
"Zucchini",
"Papaya",
"Tomato"
],
"sensor_modality": "rgb",
"input_data_format": "image_folder",
"annotation_format": "classLabel",
"num_images": 5246,
"augmented_num_images": 22875,
"augmented_zip_size_bytes": 2363305370,
"classes": [
"Alternaria_Leaf_Blight",
"Angular_Leaf_Spot",
"Anthracnose",
"Bacterial_Blight",
"Carica_Insect_Hole",
"Curled_Yellow_Spot",
"Downy_Mildew",
"Dry_Leaf",
"Early_Alternaria_Leaf_Blight",
"Fungal_Damage_Leaf",
"Healthy",
"Healthy_leaf",
"Insect_Damage",
"Iron_Chlorosis_Damage",
"Mosaic",
"Mosaic_Virus",
"Pathogen_symptoms",
"Spot",
"White_spot",
"Xanthomonas_Leaf_Spot",
"Yellow_Mosaic_Virus",
"Yellow_Necrotic_Spots_Holes"
],
"license": "cc-by-4.0",
"documentation": "https://doi.org/10.1016/j.dib.2026.112528",
"citation": "Billah, Md Masum; Rahman, Md.Anisur; Sagor, Saifuddin ; Parvin, Sanzida; Uddin, Mohammad Shorif (2026), “Agri-Vision4: A Comprehensive Multi-Crop Leaf Disease Dataset of Tomato, Papaya, Zucchini, and Bottle Gourd from Bangladesh”, Mendeley Data, V1, doi: 10.17632/8t6k37ztxc.1",
"zip_size_bytes": 5171273457,
"source": "huggingface",
"hf_link": "https://huggingface.co/datasets/Project-AgML/AgriVision4_disease_classification",
"examples_image_url": "/img/agml/sample_images/AgriVision4_disease_classification_sample.webp"
},
{
"name": "FruitVision_quality_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "quality_classification",
"location": "Bangladesh",
"environment": "lab",
"real_or_synthetic": "real",
"crop_types": [
"Apple",
"Banana",
"mango",
"Orange",
"grapes"
],
"sensor_modality": "rgb",
"input_data_format": "image_folder",
"annotation_format": "classLabel",
"num_images": 10154,
"augmented_num_images": 73389,
"augmented_zip_size_bytes": 4508560918,
"classes": [
"Formalin-mixed",
"Fresh",
"Rotten"
],
"license": "cc-by-Nnc-nd-4.0",
"documentation": "https://doi.org/10.1016/j.dib.2025.111752",
"citation": "Bijoy, Md Hasan Imam; Tasnim, Syeda Zarin; Awsaf, Syed Ali; Hasan, Md Zahid (2025), “FruitVision: A Benchmark Dataset for Fresh, Rotten, and Formalin-mixed Fruit Detection”, Mendeley Data, V2, doi: 10.17632/xkbjx8959c.2",
"zip_size_bytes": 923949864,
"source": "huggingface",
"hf_link": "https://huggingface.co/datasets/Project-AgML/FruitVision_quality_classification",
"examples_image_url": "/img/agml/sample_images/FruitVision_quality_classification_sample.webp"
},
{
"name": "plum_leaf_fruit_disease_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "disease_classification",
"location": "Bangladesh",
"environment": "lab",
"real_or_synthetic": "real",
"crop_types": [
"plum"
],
"sensor_modality": "rgb",
"input_data_format": "image_folder",
"annotation_format": "classLabel",
"num_images": 3554,
"augmented_num_images": 18000,
"augmented_zip_size_bytes": 229084152,
"classes": [
"Dead Leaf",
"Healthy Fruit",
"Healthy Leaf",
"Insect Hole",
"Unhealthy Fruit",
"Yellow"
],
"license": "cc-by-4.0",
"documentation": "https://doi.org/10.1016/j.dib.2025.111625",
"citation": "Nayeem, Rejowan Arifin; Muhib, S.M. Abdullah Al; Marjan, Shahriar; Bijoy, Md Hasan Imam; Assaduzzaman, Md (2025), “A Comprehensive Image Dataset of Plum Leaf and Fruit for Disease Detection and Classification”, Mendeley Data, V1, doi: 10.17632/w7sdx55m7z.1",
"zip_size_bytes": 7375410409,
"source": "huggingface",
"hf_link": "https://huggingface.co/datasets/Project-AgML/plum_leaf_fruit_disease_classification",
"examples_image_url": "/img/agml/sample_images/plum_leaf_fruit_disease_classification_sample.webp"
},
{
"name": "mandarin_leaf_variety_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "variety_classification",
"location": "Bangladesh",
"environment": "field",
"real_or_synthetic": "real",
"crop_types": [
"Mandarin"
],
"sensor_modality": "rgb",
"input_data_format": "image_folder",
"annotation_format": "classLabel",
"num_images": 1917,
"augmented_num_images": 8032,
"classes": [
"China Mishti",
"Darjeling",
"Mandaring",
"Nagpuri"
],
"license": "cc-by-4.0",
"documentation": "https://doi.org/10.1016/j.dib.2025.111685",
"citation": "Ahmed, Imtiaz ; Ahmed, Mahin ; Rahman, Mushfiqur; Mohsin, Dr. Sayed Mohammad (2024), “Comprehensive Dataset of Mandarin Leaf Varieties.”, Mendeley Data, V1, doi: 10.17632/8bvv2pr2d3.1",
"zip_size_bytes": 0,
"augmented_zip_size_bytes": 0,
"source": "huggingface",
"hf_link": "https://huggingface.co/datasets/Project-AgML/mandarin_leaf_variety_classification"
},
{
"name": "carambola_disease_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "disease_classification",
"location": "Bangladesh",
"environment": "lab",
"real_or_synthetic": "real",
"crop_types": [
"Carambola"
],
"sensor_modality": "rgb",
"input_data_format": "image_folder",
"annotation_format": "classLabel",
"num_images": 2618,
"augmented_num_images": 15000,
"augmented_zip_size_bytes": 4050274442,
"classes": [
"Healthy Fruits",
"Healthy Leaves",
"Insect Hole leaves",
"Unhealthy Fruits",
"Yellow Leaves"
],
"license": "cc-by-4.0",
"documentation": "https://doi.org/10.1016/j.dib.2025.111679",
"citation": "Muhib, S.M. Abdullah Al; Nayeem, Rejowan Arifin; Mezi, Noman; Emon, Nafiz Ahmed (2025), “Carambola Leaf & Fruit Dataset for Disease Detection and Classification”, Mendeley Data, V1, doi: 10.17632/f35jp46gms.1",
"zip_size_bytes": 2582982432,
"source": "huggingface",
"hf_link": "https://huggingface.co/datasets/Project-AgML/carambola_disease_classification",
"examples_image_url": "/img/agml/sample_images/carambola_disease_classification_sample.webp"
},
{
"name": "potato_leaf_blight_classification",
"machine_learning_task": "image_classification",
"agricultural_task": "disease_classification",
"location": "Tanzania",
"environment": "field",
"real_or_synthetic": "real",
"crop_types": [
"Potato"
],
"sensor_modality": "rgb",
"input_data_format": "image_folder",
"annotation_format": "classLabel",
"num_images": 58709,
"classes": [
"earlyblt",
"healthy",
"lateblt"
],
"license": "cc-by-4.0",
"documentation": "https://doi.org/10.1016/j.dib.2025.111549",
"citation": "Laizer, H., Mduma, N., Machuve, D., Lyimo, T., Babirye, C., Swai, J., & Siwingwa, A. (2023). Irish Potato Imagery Dataset for Early Detection of Crop Diseases (Version 01) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8286529",
"zip_size_bytes": 7901195460,
"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"
}
]
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