diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index 2d4b0ac..e67746d 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -29006,5 +29006,165 @@ "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/image_safari_yam_iita_osogbo", "examples_image_url": "/img/agml/sample_images/image_safari_yam_iita_osogbo_sample.webp" + }, + { + "name": "escayard_vine_esca", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "Vineyards B7 and B9, Tomiño, Pontevedra, Galicia, Spain" + ], + "lat_lon": [], + "country": "Spain", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grape" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Apple iPhone X smartphone", + "Xiaomi Poco X3 Pro smartphone (datasheet: Poco Phone X4)" + ], + "collection_period": "July 14-15, 2022", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 253, + "classes": [ + "NO", + "YES" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2024.110497", + "citation": "Vélez, S., Ariza-Sentís, M., & Valente, J. (2023). *Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10362568", + "bibtex": "@article{vlez2024escayard,\n title={EscaYard: Precision viticulture multimodal dataset of vineyards affected by Esca disease consisting of geotagged smartphone images, phytosanitary status, UAV 3D point clouds and Orthomosaics},\n author={Vélez, Sergio and Ariza-Sentís, Mar and Valente, João},\n journal={Data in Brief},\n volume={54},\n pages={110497},\n year={2024},\n publisher={Elsevier}\n}", + "zip_size_bytes": 1536189879, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/escayard_vine_esca", + "examples_image_url": "/img/agml/sample_images/escayard_vine_esca_sample.webp" + }, + { + "name": "gvsdb_grapevine_virus", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "Ensenada, Baja California, Mexico" + ], + "lat_lon": [], + "country": "Mexico", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [ + "grape" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "smartphone cameras (not specified in the record)" + ], + "collection_period": "2022-2023", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 3195, + "classes": [ + "asymptomatic_or_negative", + "symptomatic_or_positive" + ], + "license": "cc-by-nc-4.0", + "documentation": "", + "citation": "García, K., & Carrillo Tripp, J. (2025). *Grapevine Virus and Symptom Database (GVS-DB)* [Dataset]. Mendeley Data. https://doi.org/10.17632/WKBD3WSJPJ.1", + "bibtex": "", + "zip_size_bytes": 105041922, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/gvsdb_grapevine_virus", + "examples_image_url": "/img/agml/sample_images/gvsdb_grapevine_virus_sample.webp" + }, + { + "name": "gvlid_grapevine_leaf_disease", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "28 vineyards east of Pune, Maharashtra, India (metadata GPS 18.73-18.83 N, 73.97-74.07 E)" + ], + "lat_lon": [ + "18.78, 74.02" + ], + "country": "India", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grape" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Redmi Note 10 5G smartphone (Sony IMX582 sensor), per the metadata" + ], + "collection_period": "2023-06-15 per the metadata (all photos)", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 3477, + "classes": [ + "black_rot", + "esca", + "healthy", + "leaf_blight" + ], + "license": "cc-by-4.0", + "documentation": "", + "citation": "Shikalgar, A., Savalkar, A., Bhasme, A., Chavan, S., & Nikam, V. (2026). *GVLiD: GrapeVine Leaf identification of the Diseases* [Dataset]. Mendeley Data. https://doi.org/10.17632/WKYMF8BHCG.5", + "bibtex": "", + "zip_size_bytes": 902225946, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/gvlid_grapevine_leaf_disease", + "examples_image_url": "/img/agml/sample_images/gvlid_grapevine_leaf_disease_sample.webp" + }, + { + "name": "grapevine_leaf_disease_portugal", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "Folhadela, Vila Real, Portugal", + "Mateus, Vila Real, Portugal", + "Refóios do Lima, Ponte de Lima, Portugal", + "Abaças, Vila Real, Portugal" + ], + "lat_lon": [ + "41.2869, -7.7357", + "41.2944, -7.7148", + "41.7928, -8.5390", + "41.2266, -7.6991" + ], + "country": "Portugal", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grape" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Xiaomi Redmi Note 12 Pro smartphone (108MP, f/1.9, 1/1.52'', 0.7 µm native, 2.1 µm effective, 6-element lens)" + ], + "collection_period": "2024-2025", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 5267, + "classes": [ + "Downy Mildew", + "Erineum Mite", + "Esca Complex", + "Healthy", + "Powdery Mildew" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2026.112743", + "citation": "Portela, F. T., Gabriel, C., Ferreira, L., Paredes, C. A., Sousa, J. J., Peres, E., Morais, R., & Pádua, L. (2025). *Grapevine Leaves RGB Images of Disease Symptons (Esca, Downy Mildew, Powdery Mildew, and Erineum Mite)* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17343474", + "bibtex": "@article{portela2026dataset,\n title={Dataset of RGB images of healthy grapevine leaves and with downy mildew, powdery mildew, Esca complex, and erineum mite symptoms},\n author={Portela, Fernando and Carneiro, Gabriel and Ferreira, Leilson and Paredes, Cláudio A and Sousa, Joaquim J and Peres, Emanuel and Morais, Raul and Pádua, Luís},\n journal={Data in Brief},\n volume={66},\n pages={112743},\n year={2026},\n publisher={Elsevier}\n}", + "zip_size_bytes": 770180001, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/grapevine_leaf_disease_portugal", + "examples_image_url": "/img/agml/sample_images/grapevine_leaf_disease_portugal_sample.webp" } -] \ No newline at end of file +] diff --git a/static/img/agml/sample_images/escayard_vine_esca_sample.webp b/static/img/agml/sample_images/escayard_vine_esca_sample.webp new file mode 100644 index 0000000..7131481 Binary files /dev/null and b/static/img/agml/sample_images/escayard_vine_esca_sample.webp differ diff --git a/static/img/agml/sample_images/grapevine_leaf_disease_portugal_sample.webp b/static/img/agml/sample_images/grapevine_leaf_disease_portugal_sample.webp new file mode 100644 index 0000000..5705673 Binary files /dev/null and b/static/img/agml/sample_images/grapevine_leaf_disease_portugal_sample.webp differ diff --git a/static/img/agml/sample_images/gvlid_grapevine_leaf_disease_sample.webp b/static/img/agml/sample_images/gvlid_grapevine_leaf_disease_sample.webp new file mode 100644 index 0000000..4e38c37 Binary files /dev/null and b/static/img/agml/sample_images/gvlid_grapevine_leaf_disease_sample.webp differ diff --git a/static/img/agml/sample_images/gvsdb_grapevine_virus_sample.webp b/static/img/agml/sample_images/gvsdb_grapevine_virus_sample.webp new file mode 100644 index 0000000..a0ef160 Binary files /dev/null and b/static/img/agml/sample_images/gvsdb_grapevine_virus_sample.webp differ