new datasets

static/data/hf_datasets.json

@@ -6825,5 +6825,323 @@
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/vineyard_grape_segmentation",
     "examples_image_url": "/img/agml/sample_images/vineyard_grape_segmentation_sample.webp"
+  },
+  {
+    "name": "bean_disease_classification_tanzania",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Tanzania",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "bean"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 59071,
+    "classes": [
+      "anthracnose",
+      "healthy",
+      "rust"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110508",
+    "citation": "Laizer, H., Mduma, N., Machuve, D., Lyimo, T., Babirye, C., Swai, J.& Siwingwa, A. (2023). Common Beans Imagery Dataset for Early Detection of Crop Diseases (Version 01) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.8286126",
+    "zip_size_bytes": 7521678962,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/bean_disease_classification_tanzania",
+    "examples_image_url": "/img/agml/sample_images/bean_disease_classification_tanzania_sample.webp"
+  },
+  {
+    "name": "ERWIAM_blight_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "disease_detection",
+    "location": "Germany",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "apple"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 1698,
+    "classes": [
+      "Flower",
+      "Shoot",
+      "Maybe",
+      "Leaf"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110826",
+    "citation": "Maß, Virginia; Alirezazadeh, Pendar; Seidl-Schulz, Johannes; Leipnitz, Matthias; Fritzsche, Eric; Ibraheem, Rasheed Ali Adam; Geyer, Martin; Pflanz, Michael; Reim, Stefanie (2024), “ERWIAM dataset”, Mendeley Data, V1, doi: 10.17632/fpmnncmg84.1",
+    "zip_size_bytes": 393025279,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/ERWIAM_blight_detection",
+    "examples_image_url": "/img/agml/sample_images/ERWIAM_blight_detection_sample.webp"
+  },
+  {
+    "name": "fruitseg30_segmentation",
+    "machine_learning_task": "semantic_segmentation",
+    "agricultural_task": "crop_segmentation",
+    "location": "Malaysia",
+    "environment": "lab",
+    "real_or_synthetic": "real",
+    "crop_types": [],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "segmentationMask",
+    "num_images": 1969,
+    "classes": [],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110821",
+    "citation": "Shamrat, F M Javed Mehedi; Shakil, Rashiduzzaman ; Idris, Mohd Yamani Idna ; Akter, Bonna; Zhou, Xujuan  (2024), “FruitSeg30_Segmentation Dataset & Mask Annotations”, Mendeley Data, V3, doi: 10.17632/vkht8pfsp3.3",
+    "zip_size_bytes": 84278098,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/fruitseg30_segmentation",
+    "examples_image_url": "/img/agml/sample_images/fruitseg30_segmentation_sample.webp"
+  },
+  {
+    "name": "grapevine_esca_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Italy",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "grapevine"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 1770,
+    "classes": [
+      "esca",
+      "healthy"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2021.106809",
+    "citation": "Alessandrini, Michele; Calero Fuentes Rivera, Romel ; Falaschetti, Laura; Pau, Danilo; Tomaselli, Valeria; Turchetti, Claudio (2021), “ESCA-dataset”, Mendeley Data, V1, doi: 10.17632/89cnxc58kj.1",
+    "zip_size_bytes": 943834760,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/grapevine_esca_classification",
+    "examples_image_url": "/img/agml/sample_images/grapevine_esca_classification_sample.webp"
+  },
+  {
+    "name": "LSID_bean_segmentation",
+    "machine_learning_task": "semantic_segmentation",
+    "agricultural_task": "crop_segmentation",
+    "location": "Brazil",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "bean"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "segmentationMask",
+    "num_images": 6981,
+    "classes": [],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2025.111328",
+    "citation": "Florentino, Karla; Rozatto, Paulo; Sousa, Kaio; Hudson, Lucas; Meyer, Artur; Silva, Alemilson; Mendes, Igor; Borges, Alex; Morais, Leandro; Maciel, Luiz; Villela, Saulo; Pedrini, Hélio; Vieira, Marcelo (2025), “Leaf on Stem Image Dataset Beans (LSID-Beans)”, Mendeley Data, V3, doi: 10.17632/f42hwwrpgn.3",
+    "zip_size_bytes": 936089769,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/LSID_bean_segmentation",
+    "examples_image_url": "/img/agml/sample_images/LSID_bean_segmentation_sample.webp"
+  },
+  {
+    "name": "maize_tomato_weed_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Spain",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "Maize",
+      "Tomato"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 55828,
+    "classes": [
+      "atriplex",
+      "chenopodium",
+      "convolvulus",
+      "cyperus",
+      "datura",
+      "lolium",
+      "maize",
+      "portulaca",
+      "salsola",
+      "solanum",
+      "sorghum",
+      "tomato"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.111203",
+    "citation": "Mesías-Ruiz, G. A., Peña Barragán, J. M., Castro, A. I. D., & Dorado, J. (2024). DIWEED: Drone Imagery dataset for early-season WEED classification [Data set]. DIGITAL.CSIC. http://doi.org/10.20350/DIGITALCSIC/16559",
+    "zip_size_bytes": 646702758,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/maize_tomato_weed_classification",
+    "examples_image_url": "/img/agml/sample_images/maize_tomato_weed_classification_sample.webp"
+  },
+  {
+    "name": "oil_palm_fruit_ripeness_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "maturity_classification",
+    "location": "Malaysia",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "Oil Palm"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 466,
+    "classes": [
+      "Damaged",
+      "Empty",
+      "Overripe",
+      "Ripe",
+      "Unripe"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110667",
+    "citation": "MunirahRosbi. (2024). MunirahRosbi/Outdoor-Tenera-Oil-Palm-Fruit-Image: FFB Dataset (Version v1) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.11114885",
+    "zip_size_bytes": 1165711283,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/oil_palm_fruit_ripeness_classification",
+    "examples_image_url": "/img/agml/sample_images/oil_palm_fruit_ripeness_classification_sample.webp"
+  },
+  {
+    "name": "onionfoliageset_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "crop_detection",
+    "location": "Colombia",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "Green Onion"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 245,
+    "classes": [
+      "Grenn Onion",
+      "Flower",
+      "No Crop"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110679",
+    "citation": "Restrepo-Arias, J. F., Branch Bedoya, J. W.& Arregocés-Guerra, P. (2024). OnionFoliageSET [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10995125",
+    "zip_size_bytes": 1350976629,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/onionfoliageset_detection",
+    "examples_image_url": "/img/agml/sample_images/onionfoliageset_detection_sample.webp"
+  },
+  {
+    "name": "seasveg_classification_bd",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "crop_classification",
+    "location": "Bangladesh",
+    "environment": "Lab",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "Abelmoschus esculentus",
+      "Brassica oleracea",
+      "Carica Papaya",
+      "Lablab purpureus",
+      "Momordica charantia",
+      "Momordica dioica",
+      "Raphanus sativus",
+      "Solanum Iycopersicum",
+      "Trichosanthes cucumerina",
+      "Trichosanthes dioica"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 1504,
+    "augmented_num_images": 3001,
+    "augmented_zip_size_bytes": 2746734800,
+    "classes": [
+      "Abelmoschus esculentus",
+      "Brassica oleracea",
+      "Carica Papaya",
+      "Lablab purpureus",
+      "Momordica charantia",
+      "Momordica dioica",
+      "Raphanus sativus",
+      "Solanum Iycopersicum",
+      "Trichosanthes cucumerina",
+      "Trichosanthes dioica"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110564",
+    "citation": "Bappy, Md Tusher Ahmad; Hasan Rabbi, Kazi Mehedi ; Ahmed, Md Jonayed; Zeesan, Md Mahin; Rahman, Md. Wahidur (2023), “SeasVeg: An Image Dataset of Bangladeshi Seasonal Vegetables”, Mendeley Data, V1, doi: 10.17632/s6gyxb2cg9.1",
+    "zip_size_bytes": 2634005075,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/seasveg_classification_bd",
+    "examples_image_url": "/img/agml/sample_images/seasveg_classification_bd_sample.webp"
+  },
+  {
+    "name": "sunflower_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "crop_detection",
+    "location": "Bangladesh",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "sunflower"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 1649,
+    "augmented_num_images": 4286,
+    "augmented_zip_size_bytes": 1290669938,
+    "classes": [],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2025.111417",
+    "citation": "Rashid, Mohammad Rifat Ahmmad; Fahim, MD. ; Hossain, Md. Shafayat ; Alindo, Tahzib-E- (2024), “Sunflower image dataset from Bangladesh”, Mendeley Data, V1, doi: 10.17632/txct4k36ct.1",
+    "zip_size_bytes": 790925564,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/sunflower_detection",
+    "examples_image_url": "/img/agml/sample_images/sunflower_detection_sample.webp"
+  },
+  {
+    "name": "TealeafAgeQuality_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "crop_detection",
+    "location": "Bangladesh",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "Tea"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 2208,
+    "augmented_num_images": 5740,
+    "augmented_zip_size_bytes": 308849510,
+    "classes": [
+      "T1",
+      "T2",
+      "T3",
+      "T4"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110462",
+    "citation": "Kabir, Md Mohsin; Hafiz, Md Sadman ; Bandyopadhyaa, Shattik ; Jim, Jamin Rahman; Mridha, Firoz (2024), “TeaLeafAgeQuality: Age-Stratified Tea Leaf Quality Classification Dataset”, Mendeley Data, V1, doi: 10.17632/7t964jmmy3.1",
+    "zip_size_bytes": 125101435,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/TealeafAgeQuality_detection",
+    "examples_image_url": "/img/agml/sample_images/TealeafAgeQuality_detection_sample.webp"
   }
 ]