diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index 44fa4b3..f8920ec 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -491,7 +491,6 @@ "input_data_format": "image_folder", "annotation_format": "classLabel", "num_images": 18248, - "augmented_num_images": 0, "classes": [ "Early_Blight", "Healthy", @@ -502,7 +501,6 @@ "documentation": "https://doi.org/10.1016/j.dib.2025.111869", "citation": "Yinka-Banjo, Chika; Nwaneto, Chidiebere; Ugot, Ogban-Asuquo; Umeugochukwu, Obiageli; Annor, Thompson (2024), “An Image Dataset of Taro Leaf Blight Disease Collected from the West African Sub-Region”, Mendeley Data, V2, doi: 10.17632/3knm93dkc5.2", "zip_size_bytes": 1275148970, - "augmented_zip_size_bytes": 0, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/taro_blight_stage_classification", "examples_image_url": "/img/agml/sample_images/taro_blight_stage_classification_sample.png" @@ -519,7 +517,6 @@ "input_data_format": "image_folder", "annotation_format": "classLabel", "num_images": 1390, - "augmented_num_images": 0, "classes": [ "Dieback", "Fresh", @@ -531,7 +528,6 @@ "documentation": "https://doi.org/10.1016/j.dib.2025.112334", "citation": "Islam, Md. Masudul; Sheikh, Md Ripon, 2025, Jute Disease Image Dataset, https://doi.org/10.7910/DVN/FJ1DM1, Harvard Dataverse, V1", "zip_size_bytes": 391487547, - "augmented_zip_size_bytes": 0, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/jute_disease_classification", "examples_image_url": "/img/agml/sample_images/jute_disease_classification.png" @@ -548,7 +544,6 @@ "input_data_format": "image_folder", "annotation_format": "classLabel", "num_images": 2726, - "augmented_num_images": 0, "classes": [ "Bacterial Leaf Spot", "Downy Mildew", @@ -559,7 +554,6 @@ "documentation": "https://doi.org/10.1016/j.dib.2025.111716", "citation": "Dharrao, Madhuri; Dharrao, Deepak; Sonawane, Rakesh; zade, Nilima (2025), “Niphad Grape Leaf Disease Dataset (NGLD)”, Mendeley Data, V5, doi: 10.17632/8nnd2ypcv3.5", "zip_size_bytes": 26151761, - "augmented_zip_size_bytes": 0, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/grape_leaf_disease_classification", "examples_image_url": "/img/agml/sample_images/grape_leaf_disease_classification.png" @@ -576,7 +570,6 @@ "input_data_format": "image_folder", "annotation_format": "classLabel", "num_images": 2595, - "augmented_num_images": 0, "classes": [ "Leaf_Algal", "Leaf_Blight", @@ -589,10 +582,9 @@ "documentation": "https://doi.org/10.1016/j.dib.2025.111845", "citation": "Thanh Truong, Nguyen; 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"documentation": "https://doi.org/10.1016/j.dib.2025.111379", + "citation": "Ahmad, MD Hasan (2024), “Advanced Tea Crop Disease Study: High-Resolution Dataset for Precision Agriculture and Pathological Insight”, Mendeley Data, V4, doi: 10.17632/tt2smzrzrs.4", + "zip_size_bytes": 258979082, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/tea_leaf_disease_classification_bangladesh", + "examples_image_url": "/img/agml/sample_images/tea_leaf_disease_classification_bangladesh_sample.png" + }, + { + "name": "vegetable_classification_bangladesh_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "crop_classification", + "location": "Bangladesh", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "Bean", + "Bitter melon", + "Brinjal", + "Cucumber", + "Garlic", + "Green Chili", + "Ladies finger", + "Onion", + "Pointed gourd", + "Potato", + "Radish", + "Tomato" + ], + "sensor_modality": "rgb", + 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