diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index 26fa999..26a1731 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -11564,5 +11564,224 @@ "Private Data (field surveys)" ] } + }, + { + "name": "maize_nitrogen_deficiency_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "Agricultural Institute Osijek, Osijek, Croatia" + ], + "country": "Croatia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon 80D" + ], + "collection_period": "July 2023", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 1200, + "classes": [ + "0", + "75", + "136" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109625", + "citation": "Galic, Vlatko; Podnar Zarko, Ivana; Novoselnik, Filip; Salaic, Miroslav (2023), “Nitrogen deficiency in maize: annotated image classification dataset”, Mendeley Data, V1, doi: 10.17632/g7xnn2bm4g.1", + "zip_size_bytes": 514189935, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/maize_nitrogen_deficiency_classification", + "examples_image_url": "/img/agml/sample_images/maize_nitrogen_deficiency_classification_sample.webp" + }, + { + "name": "guava_damage_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "damage_classification", + "location": [ + "Velammal Engineering College, Chennai, Tamilnadu, India" + ], + "lat_lon": [ + "13 09 04.9, 80 11 29.6" + ], + "country": "India", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "guava" + ], + "sensor_modality": "rgb, thermal", + "imaging_equipment": [ + "Samsung M31S mobile camera", + "FLIR One Pro" + ], + "collection_period": "", + "platform": "mobile", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 3959, + "classes": [ + "15cm_drop", + "30cm_drop", + "45cm_drop", + "chilling_injured", + "diseased", + "healthy", + "mixed_drop" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109486", + "citation": "P, PATHMANABAN; B K, GNANAVEL; Anandan, Shanmuga Sundaram (2023), “Guava (Psidium guajava) fruit digital and thermal Images”, Mendeley Data, V1, doi: 10.17632/5kptnn7ycr.1", + "zip_size_bytes": 11174264286, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/guava_damage_classification", + "examples_image_url": "/img/agml/sample_images/guava_damage_classification_sample.webp" + }, + { + "name": "banana_disease_classification_tanzania", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "Kagera, Tanzania", + "Arusha, Tanzania", + "Dar es Salaam, Tanzania", + "Kilimanjaro, Tanzania", + "Mbeya, Tanzania" + ], + "country": "Tanzania", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "banana" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Samsung Galaxy 01 smartphone, 13-megapixel" + ], + "collection_period": "February 2021 to July 2021", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 16092, + "classes": [ + "black_sigatoka", + "fusarium_wilt", + "healthy" + ], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109322", + "citation": "Mduma, N., Laizer, H., Loyani, L., Macheli, M., Msengi, Z., Karama, A., Msaki, I., Sanga, S., Jomanga, K., & Judith, L. (2022). The Nelson Mandela African Institution of Science and Technology Bananas dataset (Version V6) [dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/LQUWXW", + "zip_size_bytes": 4631882825, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/banana_disease_classification_tanzania", + "examples_image_url": "/img/agml/sample_images/banana_disease_classification_tanzania_sample.webp" + }, + { + "name": "greenhouse_leafy_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "crop_segmentation", + "location": [ + "United States of America" + ], + "country": "United States of America", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "romaine lettuce" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Azure Kinect DK; 12-megapixel sensor", + "Raspberry Pi Camera Module v3; 12-megapixel; 120° wide-angle" + ], + "collection_period": "", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 3348, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.compag.2024.109711", + "citation": "Jonathan Cárdenas. (2024). JSCG95/leafy-segmentation_CEA: Data availability release (Version V1.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.11110409", + "zip_size_bytes": 86412781, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/greenhouse_leafy_segmentation", + "examples_image_url": "/img/agml/sample_images/greenhouse_leafy_segmentation_sample.webp" + }, + { + "name": "litchi_fruit_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Litchi Culture Expo Park, Conghua District, Guangzhou, China" + ], + "lat_lon": [ + "113.618, 23.583" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "litchi" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Phantom 4 Unmanned Aerial Vehicle" + ], + "collection_period": "May 14, 2024, July 2, 2024, and May 24, 2025", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 1880, + "classes": [ + "0" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.atech.2026.102159", + "citation": "Li, W. (2026). Litchi-UAV: A UAV-based Litchi Fruit Detection Dataset for Precision Agriculture [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.19364014", + "zip_size_bytes": 1116377210, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/litchi_fruit_detection", + "examples_image_url": "/img/agml/sample_images/litchi_fruit_detection_sample.webp" + }, + { + "name": "maize_individual_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "University of Missouri Genetics Farm, Columbia, MO, United States of America" + ], + "country": "United States of America", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Mavic 2 Pro, L1D-20c Hasselblad camera (20-megapixel)" + ], + "collection_period": "June 16 and June 23, 2021", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 27, + "classes": [ + null, + "maize" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.atech.2026.102421", + "citation": "Sangjan, W., Pandey, P., Best, N., & Washburn, J. (2025). MatchPlant: An Open-Source Pipeline for UAV-Based Single-Plant Detection from Undistorted Images with Orthomosaic Projection [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.14856123", + "zip_size_bytes": 1231488169, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/maize_individual_detection", + "examples_image_url": "/img/agml/sample_images/maize_individual_detection_sample.webp" } -] \ No newline at end of file +] diff --git a/static/img/agml/sample_images/banana_disease_classification_tanzania_sample.webp b/static/img/agml/sample_images/banana_disease_classification_tanzania_sample.webp new file mode 100644 index 0000000..36d28cb Binary files /dev/null and b/static/img/agml/sample_images/banana_disease_classification_tanzania_sample.webp differ diff --git a/static/img/agml/sample_images/greenhouse_leafy_segmentation_sample.webp b/static/img/agml/sample_images/greenhouse_leafy_segmentation_sample.webp new file mode 100644 index 0000000..808aa91 Binary files /dev/null and b/static/img/agml/sample_images/greenhouse_leafy_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/guava_damage_classification_sample.webp b/static/img/agml/sample_images/guava_damage_classification_sample.webp new file mode 100644 index 0000000..7ff58bd Binary files /dev/null and b/static/img/agml/sample_images/guava_damage_classification_sample.webp differ diff --git a/static/img/agml/sample_images/litchi_fruit_detection_sample.webp b/static/img/agml/sample_images/litchi_fruit_detection_sample.webp new file mode 100644 index 0000000..42a5f61 Binary files /dev/null and b/static/img/agml/sample_images/litchi_fruit_detection_sample.webp differ diff --git a/static/img/agml/sample_images/maize_individual_detection_sample.webp b/static/img/agml/sample_images/maize_individual_detection_sample.webp new file mode 100644 index 0000000..41d67c8 Binary files /dev/null and b/static/img/agml/sample_images/maize_individual_detection_sample.webp differ diff --git a/static/img/agml/sample_images/maize_nitrogen_deficiency_classification_sample.webp b/static/img/agml/sample_images/maize_nitrogen_deficiency_classification_sample.webp new file mode 100644 index 0000000..0522451 Binary files /dev/null and b/static/img/agml/sample_images/maize_nitrogen_deficiency_classification_sample.webp differ