new datasets

.gitignore

@@ -18,3 +18,5 @@
 npm-debug.log*
 yarn-debug.log*
 yarn-error.log*
+
+.external

static/data/hf_datasets.json

@@ -7143,5 +7143,289 @@
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/TealeafAgeQuality_detection",
     "examples_image_url": "/img/agml/sample_images/TealeafAgeQuality_detection_sample.webp"
+  },
+  {
+    "name": "ACHENY_variety_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "variety_classification",
+    "location": "Iran",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "chenopod"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 27030,
+    "classes": [
+      "AnaHau",
+      "AnaSet",
+      "AtrCan",
+      "AtrLen",
+      "AtrLeu",
+      "CerAre",
+      "CheAlb",
+      "CorMon",
+      "GirOpp",
+      "HalAmm",
+      "HalBel",
+      "HalPer",
+      "HalStr",
+      "HalSub",
+      "HamSal",
+      "KocSco",
+      "KocSte",
+      "SalAba",
+      "SalDen",
+      "SalInc",
+      "SalKal",
+      "SalKer",
+      "SalPra",
+      "SalTom",
+      "SalTur",
+      "SalYaz",
+      "SeiCin",
+      "SeiRos",
+      "SuaAcu",
+      "SuaAeg"
+    ],
+    "license": "cc0 1.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2021.107478",
+    "citation": "Heidary-Sharifabad, Ahmad; Sardari Zarchi, Mohsen; Emadi, Sima; Zarei, Gholamreza (2021), “ACHENY : A Standard Chenopodiaceae Image dataset  for Deep Learning Models”, Mendeley Data, V1, doi: 10.17632/fpfty8nn7j.1",
+    "zip_size_bytes": 1036414540,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/ACHENY_variety_classification",
+    "examples_image_url": "/img/agml/sample_images/ACHENY_variety_classification_sample.webp"
+  },
+  {
+    "name": "citrus_fruit_leaf_disease_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Pakistan",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "Citrus"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 759,
+    "classes": [
+      "black_spot",
+      "canker",
+      "greening",
+      "healthy",
+      "melanose",
+      "scab"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2019.104340",
+    "citation": "Rauf, Hafiz Tayyab; Saleem, Basharat ALi ; Lali, M. Ikram Ullah ; Khan, Muhammad Attique ; Sharif, Muhammad ; Bukhari, Syed Ahmad Chan  (2019), “A Citrus Fruits and Leaves Dataset for Detection and Classification of Citrus Diseases through Machine Learning”, Mendeley Data, V2, doi: 10.17632/3f83gxmv57.2",
+    "zip_size_bytes": 67503676,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/citrus_fruit_leaf_disease_classification",
+    "examples_image_url": "/img/agml/sample_images/citrus_fruit_leaf_disease_classification_sample.webp"
+  },
+  {
+    "name": "crop_weed_detection_latvia",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "crop_detection",
+    "location": "Latvia",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 1176,
+    "classes": [
+      "crop",
+      "weed"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2020.105833",
+    "citation": "Sudars, Kaspars (2021), “Data for: Dataset of annotated food crops and weed images for robotic computer vision control”, Mendeley Data, V1, doi: 10.17632/nj4vtk4tt6.1",
+    "zip_size_bytes": 384812437,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/crop_weed_detection_latvia",
+    "examples_image_url": "/img/agml/sample_images/crop_weed_detection_latvia_sample.webp"
+  },
+  {
+    "name": "fortunella_margarita_growth_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "maturity_detection",
+    "location": "Taiwan",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "fortunella margarita"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 6611,
+    "classes": [
+      "growing",
+      "immature",
+      "mature"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2021.107293",
+    "citation": "Wu, Yi-Shun; Huang, Mei Ling (2021), “A dataset of fortunella margarita images”, Mendeley Data, V1, doi: 10.17632/wnv4bszczz.1",
+    "zip_size_bytes": 7253075926,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/fortunella_margarita_growth_detection",
+    "examples_image_url": "/img/agml/sample_images/fortunella_margarita_growth_detection_sample.webp"
+  },
+  {
+    "name": "maize_weed_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "crop_detection",
+    "location": "Nigeria",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "maize"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 500,
+    "classes": [
+      "maize",
+      "weed"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2023.109030",
+    "citation": "Olaniyi, Olayemi; Salaudeen, Muhammadu; Daniya, Emmanuel; Mohammed Abdullahi, Ibrahim; Folorunso, Taliha Abiodun; Bala, Jibril; Bello Kontagora, Nuhu; Adedigba, Adeyinka; Oluwole, Blessing ; Macarthy, Odunayo; Bankole, Abdullah (2022), “Maize-Weed Image Dataset ”, Mendeley Data, V2, doi: 10.17632/jjbfcckrsp.2",
+    "zip_size_bytes": 816946725,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/maize_weed_detection",
+    "examples_image_url": "/img/agml/sample_images/maize_weed_detection_sample.webp"
+  },
+  {
+    "name": "merlot_mildew_segmentation",
+    "machine_learning_task": "semantic_segmentation",
+    "agricultural_task": "disease_segmentation",
+    "location": "France",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "grape"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "segmentationMask",
+    "num_images": 23,
+    "classes": [],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2021.107250",
+    "citation": "abdelghafour, Florent; Keresztes, Barna ; Deshayes, Aymeric ; Germain, Christian ; Da Costa, Jean-Pierre (2021), “An annotated image dataset of downy mildew symptoms on Merlot grape variety”, Mendeley Data, V1, doi: 10.17632/yxf2yv5ymt.1",
+    "zip_size_bytes": 49296636,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/merlot_mildew_segmentation",
+    "examples_image_url": "/img/agml/sample_images/merlot_mildew_segmentation_sample.webp"
+  },
+  {
+    "name": "CocoaMFDB_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "crop_detection",
+    "location": "Cote d'Ivoire",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "cocoa"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 1254,
+    "augmented_num_images": 0,
+    "classes": [],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2023.109196",
+    "citation": "AYIKPA, Kacoutchy Jean (2022), “CocoaMFDB”, Mendeley Data, V2, doi: 10.17632/9msjjh3np6.2",
+    "zip_size_bytes": 0,
+    "augmented_zip_size_bytes": 0,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/CocoaMFDB_detection"
+  },
+  {
+    "name": "pomegranate_quality_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "quality_classification",
+    "location": "India",
+    "environment": "lab",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "pomegranate"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 1080,
+    "classes": [
+      "G1_Q1",
+      "G2_Q1",
+      "G3_Q1"
+    ],
+    "license": "cc0-1.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2021.107249",
+    "citation": "Dr. Vijay S Rajpurohit, and Kshitijarun Y Bidari. (2020). Pomegranate Fruit Dataset [Dataset]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/551234",
+    "zip_size_bytes": 127415030,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/pomegranate_quality_classification",
+    "examples_image_url": "/img/agml/sample_images/pomegranate_quality_classification_sample.webp"
+  },
+  {
+    "name": "QuinceSet_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "crop_detection",
+    "location": "Latvia",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "quince"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 1515,
+    "classes": [],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2022.108332",
+    "citation": "Kaufmane, E., Sudars, K., Namatēvs, I., Kalniņa, I., Judvaitis, J., Balašs, R.& Strautiņa, S. (2022). QuinceSet: Dataset of Annotated Japanese Quince Images for Object Detection. Data in Brief. https://doi.org/10.5281/zenodo.6402251",
+    "zip_size_bytes": 5812912071,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/QuinceSet_detection",
+    "examples_image_url": "/img/agml/sample_images/QuinceSet_detection_sample.webp"
+  },
+  {
+    "name": "Medjool_date_ripeness_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "fruit_ripeness_detection",
+    "location": "Mexico",
+    "environment": "lab",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "date"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 1415,
+    "classes": [
+      "ripe",
+      "unripe"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2021.107116",
+    "citation": "García Vázquez , Juan Pablo; Pérez Pérez, Dalila Blanca; Salomón Torres, Ricardo (2021), “MedjoolDates_Dataset”, Mendeley Data, V2, doi: 10.17632/872xk9npmz.2",
+    "zip_size_bytes": 5010207271,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/Medjool_date_ripeness_detection",
+    "examples_image_url": "/img/agml/sample_images/Medjool_date_ripeness_classification_sample.webp"
   }
 ]