new datasets

static/data/hf_datasets.json

@@ -595,62 +595,177 @@
     "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": "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"
+  },
+  {
+    "name": "betel_leaf_disease_classification_2",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Bangladesh",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": ["Betel Leaf"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 2037,
+    "augmented_num_images": 10185,
+    "classes": [
+      "Healthy_Leaf",
+      "Leaf_Rot",
+      "Leaf_Spot"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2025.111564",
+    "citation": "Hridoy, Rashidul Hasan; Habib, Md Tarek; Mahmud, Imran; Haque, Aminul; Mamun, Md Abdulla Al (2025), “Comprehensive Betel Leaf Disease Dataset for Advanced Pathology Research ”, Mendeley Data, V1, doi: 10.17632/vpzkntzjty.1",
+    "zip_size_bytes": 202790683,
+    "augmented_zip_size_bytes": 1195037270,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/betel_leaf_disease_classification_2",
+    "examples_image_url": "/img/agml/sample_images/betel_leaf_disease_classification_2_sample.png"
+  },
+  {
+    "name": "coffee_bean_quality_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "quality_classification",
+    "location": "India",
+    "environment": "lab",
+    "real_or_synthetic": "real",
+    "crop_types": ["coffee"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 464,
+    "augmented_num_images": 0,
+    "classes": [
+      "A",
+      "AA",
+      "AAA",
+      "AB",
+      "Bits",
+      "Bulk",
+      "C",
+      "PB-I",
+      "PB-II"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2025.111434",
+    "citation": "Nair, Bipin (2024), “CBD_Coffee Bean Dataset”, Mendeley Data, V1, doi: 10.17632/52877z55vr.1",
+    "zip_size_bytes": 337175739,
+    "augmented_zip_size_bytes": 0,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/coffee_bean_quality_classification",
+    "examples_image_url": "/img/agml/sample_images/coffee_bean_quality_classification_sample.png"
+  },
+  {
+    "name": "eggplant_disease_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Bangladesh",
+    "environment": "lab",
+    "real_or_synthetic": "real",
+    "crop_types": ["eggplant"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 4089,
+    "augmented_num_images": 20353,
+    "classes": [
+      "Healthy Leaf",
+      "Insect Pest Disease",
+      "Leaf Spot Disease",
+      "Mosaic Virus Disease",
+      "White Mold Disease",
+      "Wilt Disease"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2025.111353",
+    "citation": "Howlader, Shakib; Ahamed, Md. Sabbir; Mojumdar, Mayen Uddin  (2025), “Eggplant Leaf Disease Detection Dataset”, Mendeley Data, V2, doi: 10.17632/d3ypkphghb.2",
+    "zip_size_bytes": 2711819609,
+    "augmented_zip_size_bytes": 135488596,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/eggplant_disease_classification",
+    "examples_image_url": "/img/agml/sample_images/eggplant_disease_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"
+    "name": "olive_tree_crown_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "crop_detection",
+    "location": "Morocco",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": ["Olive Tree"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 5842,
+    "augmented_num_images": 0,
+    "classes": [],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2025.111515",
+    "citation": "Hnida, Youness; Mahraz, Mohamed Adnane; Achebour, Ali; Yahyaouy, Ali; Riffi, Jamal; Tairi, Hamid (2024), “OliveTreeCrownsDb: A High-Resolution UAV Dataset for Detection and Segmentation in Agricultural Computer Vision”, Mendeley Data, V3, doi: 10.17632/xym8rd2srf.3",
+    "zip_size_bytes": 2045083876,
+    "augmented_zip_size_bytes": 0,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/olive_tree_crown_detection",
+    "examples_image_url": "/img/agml/sample_images/olive_tree_crown_detection_sample.png"
   }
+
+
 ]