new datasets

static/data/hf_datasets.json

@@ -478,5 +478,179 @@
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/RoseLeafInsight_disease_classification",
     "examples_image_url": "/img/agml/sample_images/RoseLeafInsight_disease_classification_sample.png"
-}
+  },
+  {
+    "name": "taro_blight_stage_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": ["Nigeria", "Ghana"],
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": ["taro"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 18248,
+    "augmented_num_images": 0,
+    "classes": [
+      "Early_Blight",
+      "Healthy",
+      "Late_Blight",
+      "Mid_Blight"
+    ],
+    "license": "cc-by-4.0",
+    "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"
+  },
+  {
+    "name": "jute_disease_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Bangladesh",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": ["jute"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 1390,
+    "augmented_num_images": 0,
+    "classes": [
+      "Dieback",
+      "Fresh",
+      "Holed",
+      "Mosaic",
+      "Stem Soft Rot"
+    ],
+    "license": "cc0-1.0",
+    "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"
+  },
+  {
+    "name": "grape_leaf_disease_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "India",
+    "environment": "Lab",
+    "real_or_synthetic": "real",
+    "crop_types": ["grape"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 2726,
+    "augmented_num_images": 0,
+    "classes": [
+      "Bacterial Leaf Spot",
+      "Downy Mildew",
+      "Healthy Leaves",
+      "Powdery Mildew"
+    ],
+    "license": "cc-by-4.0",
+    "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"
+  },
+  {
+    "name": "durian_disease_classification_vietnam",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Vietnam",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": ["durian"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 2595,
+    "augmented_num_images": 0,
+    "classes": [
+      "Leaf_Algal",
+      "Leaf_Blight",
+      "Leaf_Colletotrichum",
+      "Leaf_Healthy",
+      "Leaf_Phomopsis",
+      "Leaf_Rhizoctonia"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2025.111845",
+    "citation": "Thanh Truong, Nguyen; Xuan Linh, Nguyen; Thang, Cap Pham Dinh; Tuong, Le (2025), “A Durian Leaf Image Dataset of Common Diseases in Vietnam for Agricultural Diagnosis ”, Mendeley Data, V4, doi: 10.17632/pxzvksbwnj.4",
+    "zip_size_bytes": 183109270,
+    "augmented_zip_size_bytes": 0,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/durian_disease_classification_vietnam",
+    "examples_image_url": "/img/agml/sample_images/durian_disease_classification_vietnam.png"
+  },
+  {
+  "name": "teaLeafBD_disease_classification_classification",
+  "machine_learning_task": "image_classification",
+  "agricultural_task": "disease_classification",
+  "location": "Bangladesh",
+  "environment": "lab",
+  "real_or_synthetic": "real",
+  "crop_types": ["tea"],
+  "sensor_modality": "rgb",
+  "input_data_format": "image_folder",
+  "annotation_format": "classLabel",
+  "num_images": 5278,
+  "augmented_num_images": 0,
+  "classes": [
+    "Brown Blight",
+    "Gray Blight",
+    "Green mirid bug",
+    "Healthy leaf",
+    "Helopeltis",
+    "Red spider",
+    "Tea algal leaf spot"
+  ],
+  "license": "cc-by-4.0",
+  "documentation": "https://doi.org/10.1016/j.dib.2025.111769",
+  "citation": "Alam, B M Shahria; Ahammed, Fahad; Kibria, Golam; Noor, Mohammad Tahmid; Shikdar, Omar Faruq; Mahazabin, Kazi Isat; Ali, Md Nawab Yousuf (2025), “teaLeafBD”, Mendeley Data, V4, doi: 10.17632/744vznw5k2.4",
+  "zip_size_bytes": 1353513825,
+  "augmented_zip_size_bytes": 0,
+  "source": "huggingface",
+  "hf_link": "https://huggingface.co/datasets/Project-AgML/teaLeafBD_disease_classification_classification",
+  "examples_image_url": "/img/agml/sample_images/teaLeafBD_disease_classification_classification_sample.png"
+  },
+  {
+  "name": "mango_growth_detection",
+  "machine_learning_task": "object_detection",
+  "agricultural_task": "maturity_classification",
+  "location": "Bangladesh",
+  "environment": "lab",
+  "real_or_synthetic": "real",
+  "crop_types": ["mango"],
+  "sensor_modality": "rgb",
+  "input_data_format": "image_folder",
+  "annotation_format": "boundingBox",
+  "num_images": 2000,
+  "augmented_num_images": 0,
+  "classes": [
+    "Early-Fruit",
+    "Mature",
+    "Premature",
+    "Ripe"
+  ],
+  "license": "cc-by-4.0",
+  "documentation": "https://doi.org/10.1016/j.dib.2025.111780",
+  "citation": "Kabir, Sayem ; Rashid, Mohammad Rifat Ahmmad (2024), “Image Dataset for Mango Growth Stages  Analysis”, Mendeley Data, V1, doi: 10.17632/5snwpzdtzs.1",
+  "zip_size_bytes": 76123361,
+  "augmented_zip_size_bytes": 0,
+  "source": "huggingface",
+  "hf_link": "https://huggingface.co/datasets/Project-AgML/mango_growth_object_detection",
+  "examples_image_url": "/img/agml/sample_images/mango_growth_detection_sample.png"
+  }
 ]