diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index 8de94cc..2034be7 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -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" } ]