diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index b87f621..4929a0f 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -2125,7 +2125,7 @@ { "name": "banana_guava_quality_classification", "machine_learning_task": "image_classification", - "agricultural_task": "crop classification", + "agricultural_task": "quality_classification", "location": [ "Hisar, Haryana, India" ], @@ -2471,7 +2471,7 @@ { "name": "lentil_disease_classification", "machine_learning_task": "image_classification", - "agricultural_task": "disease classification", + "agricultural_task": "disease_classification", "location": [ "Farm A, Barisal, Bangladesh", "Farm B, Barisal, Bangladesh", @@ -2750,7 +2750,7 @@ { "name": "rice_grain_variety_classification", "machine_learning_task": "image_classification", - "agricultural_task": "crop classification", + "agricultural_task": "variety_classification", "location": [ "Dinajpur, Bangladesh", "Dhaka, Bangladesh" @@ -3174,7 +3174,7 @@ { "name": "sorghum_weed_classification", "machine_learning_task": "image_classification", - "agricultural_task": "weed segmentation", + "agricultural_task": "weed_classification", "location": [ "SRM Care Farm, Chengalpattu district, Tamil Nadu, India" ], @@ -4124,7 +4124,7 @@ { "name": "mango_leaf_disease_classification", "machine_learning_task": "image_classification", - "agricultural_task": "disease classification", + "agricultural_task": "disease_classification", "location": [ "Sher-e-Bangla Agricultural University mango garden, Dhaka, Bangladesh", "Jahangir Nagar University garden, Savar, Bangladesh", @@ -6118,7 +6118,7 @@ { "name": "MH_SoyaHealthVision_disease_classification_leaf", "machine_learning_task": "image_classification", - "agricultural_task": "disease detection", + "agricultural_task": "disease_classification", "location": [ "Chikhali, Sangli, Maharashtra, India", "Upavale, Sangli, Maharashtra, India", @@ -7076,7 +7076,7 @@ { "name": "okra_thermal_maturity_classification", "machine_learning_task": "image_classification", - "agricultural_task": "crop classification", + "agricultural_task": "maturity_classification", "location": [ "Chennai, Chennai, India" ], @@ -7934,7 +7934,7 @@ { "name": "CoLeaf_nutritional_deficiency_classification", "machine_learning_task": "image_classification", - "agricultural_task": "disease classification", + "agricultural_task": "disease_classification", "location": [ "San Miguel de las Naranjas, Jaén province, Cajamarca, Perú", "La Palma Central, Jaén province, Cajamarca, Perú" @@ -7980,7 +7980,7 @@ { "name": "MangoLeafBD_disease_classification", "machine_learning_task": "image_classification", - "agricultural_task": "disease classification", + "agricultural_task": "disease_classification", "location": [ "Sher-e-Bangla Agricultural University mango garden, Dhaka, Bangladesh", "Jahangir Nagar University garden, Savar, Bangladesh", @@ -8115,7 +8115,7 @@ { "name": "PFSD_Musa_banana_variety_classification", "machine_learning_task": "image_classification", - "agricultural_task": "disease classification", + "agricultural_task": "disease_classification", "location": [ "BORTARI VILLAGE, Chaygaon, Kukurmara, District - Kamrup (Rural), Assam, India", "HAJO VILLAGE, District - Kamrup (Rural), Assam, India" @@ -8526,7 +8526,7 @@ { "name": "grape_variety_classification", "machine_learning_task": "image_classification", - "agricultural_task": "crop classification", + "agricultural_task": "variety_classification", "location": [ "al_dujail, Salahaddin, Iraq", "balad, Salahaddin, Iraq", @@ -8643,7 +8643,7 @@ { "name": "soybean_damage_classification", "machine_learning_task": "image_classification", - "agricultural_task": "crop classification", + "agricultural_task": "damage_classification", "location": [ "Nanjing, Nanjing, China" ], @@ -8785,7 +8785,7 @@ { "name": "vineyard_grape_segmentation", "machine_learning_task": "semantic_segmentation", - "agricultural_task": "crop detection", + "agricultural_task": "crop_segmentation", "location": [ "Tomiño, Pontevedra, Galicia, Spain" ], @@ -9212,7 +9212,7 @@ { "name": "sunflower_detection", "machine_learning_task": "object_detection", - "agricultural_task": "crop detection", + "agricultural_task": "crop_detection", "location": [ "Nagoriakandi, Narsingdi District, Bangladesh", "Amjhupi, Meherpur district, Bangladesh" @@ -9963,7 +9963,7 @@ { "name": "corn_leaf_pest_classification", "machine_learning_task": "image_classification", - "agricultural_task": "disease segmentation", + "agricultural_task": "disease_classification", "location": [ "Keleng, Western Region, Cameroon", "Dom'meni, Western Region, Cameroon", @@ -10587,7 +10587,7 @@ { "name": "ICPTC_pistachio_tree_variety_classification", "machine_learning_task": "image_classification", - "agricultural_task": "crop classification", + "agricultural_task": "variety_classification", "location": [ "Chah-Afzal region, Ardakan County, Yazd, Iran" ], @@ -10626,7 +10626,7 @@ { "name": "citrus_fruit_variety_classification", "machine_learning_task": "image_classification", - "agricultural_task": "crop classification", + "agricultural_task": "variety_classification", "location": [ "Fengyuan District, Taichung, Taiwan", "Taiping District, Taichung, Taiwan", @@ -10709,5 +10709,425 @@ "imaging_equipment": [ "Micromax Canvas TAB P802" ] + }, + { + "name": "crop_pest_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "University of Energy and Natural Resources, Sunyani, Ghana", + "African Technology Policy Society Network, Nairobi, Kenya" + ], + "country": [ + "Ghana", + "Kenya" + ], + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cashew", + "cassava", + "maize", + "tomato" + ], + "sensor_modality": "rgb", + "platform": "handheld", + "imaging_equipment": "Canon EOS Rebel T7", + "collection_period": "October to December 2022", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 25170, + "augmented_num_images": 105252, + "augmented_zip_size_bytes": 7101568095, + "classes": [ + "anthracnose", + "bacterial blight", + "brown spot", + "fall armyworm", + "grasshoper", + "green mite", + "gumosis", + "healthy", + "leaf beetle", + "leaf blight", + "leaf curl", + "leaf miner", + "leaf spot", + "mosaic", + "red rust", + "septoria leaf spot", + "streak virus", + "verticulium wilt" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109306", + "citation": "Mensah Kwabena, Patrick; Akoto-Adjepong, Vivian; Adu, Kwabena; Abra Ayidzoe, Mighty; Asare Bediako, Elvis; Nyarko-Boateng, Owusu; Boateng, Samuel; Fobi Donkor, Esther; Umar Bawah, Faiza; Songose Awarayi, Nicodemus; Nimbe, Peter; Kofi Nti, Isaac; Abdulai, Muntala; Roger Adjei, Remember; Opoku, Michael (2023), “Dataset for Crop Pest and Disease Detection”, Mendeley Data, V1, doi: 10.17632/bwh3zbpkpv.1", + "zip_size_bytes": 1336382819, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/crop_pest_disease_classification", + "examples_image_url": "/img/agml/sample_images/crop_pest_disease_classification_sample.webp" + }, + { + "name": "FruitNet_quality_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "quality_classification", + "location": [ + "VISHWAKARMA UNIVERSITY, Survey No. 2, 3, 4 Laxmi Nagar, Kondhwa Budruk, Pune - 411 048. Maharashtra, India", + "HUBTOWN COUNTRYWOODS SOCIETY, Tilekar Nagar, Kondhwa Budruk, Pune - 411 048, Maharashtra, India" + ], + "lat_lon": [ + "18.4603, 73.8836", + "18.442866, 73.884894" + ], + "country": "India", + "environment": "Mixed", + "real_or_synthetic": "real", + "crop_types": [ + "banana", + "apple", + "guava", + "lime", + "orange", + "pomegranate" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "iPhone 6", + "Zuk Z2 Plus", + "Realme 5 Pro" + ], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 19526, + "classes": [ + "Bad", + "Good", + "Mixed" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2021.107686", + "citation": "PATIL, Kailas; MESHRAM, Vishal (2022), “FruitNet: Indian Fruits Dataset with quality (Good, Bad & Mixed quality)”, Mendeley Data, V3, doi: 10.17632/b6fftwbr2v.3", + "zip_size_bytes": 3292469625, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/FruitNet_quality_classification", + "examples_image_url": "/img/agml/sample_images/FruitNet_quality_classification_sample.webp" + }, + { + "name": "grapevine_development_stage_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "maturity_classification", + "location": [ + "Meissen, Saxony, Germany" + ], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grapes" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Blink Outdoor" + ], + "collection_period": "June to August 2022", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 3993, + "classes": [ + "71_73", + "75", + "77", + "79" + ], + "license": "cc-by-nc-sa-4.0", + "documentation": "https://doi.org/10.1016/j.compag.2023.107944", + "citation": "Schieck, M. (2022, October 31). Grapevine development stages (BBCH 71-79). Kaggle. https://www.kaggle.com/datasets/schieckmartin/grapesdevelopmentstages", + "zip_size_bytes": 18186712, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/grapevine_development_stage_classification", + "examples_image_url": "/img/agml/sample_images/grapevine_development_stage_classification_sample.webp" + }, + { + "name": "grapevine_growth_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Meissen, Saxony, Germany" + ], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grapevine" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Blink Outdoor" + ], + "collection_period": "June to August 2022", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 1212, + "classes": [ + "0" + ], + "license": "cc-by-nc-sa-4.0", + "documentation": "https://doi.org/10.1016/j.compag.2023.107944", + "citation": "Schieck, M. (2022, October 31). Grapevine development stages (BBCH 71-79). Kaggle. https://www.kaggle.com/datasets/schieckmartin/grapesdevelopmentstages", + "zip_size_bytes": 120873756, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/grapevine_growth_detection", + "examples_image_url": "/img/agml/sample_images/grapevine_growth_detection_sample.webp" + }, + { + "name": "strawberry_growth_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "maturity_detection", + "location": [ + "Plant Growth Facility, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America" + ], + "country": "United States of America", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [ + "strawberry" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "June to October 2022 and 2023", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 1477, + "classes": [ + "flower", + "green", + "overripe", + "red", + "small g", + "turning red", + "white" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.compag.2024.108911", + "citation": "Yang, Q.& Jin, Z. (2024). Dataset for 'Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision' [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10957909", + "zip_size_bytes": 6749132402, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/strawberry_growth_detection", + "examples_image_url": "/img/agml/sample_images/strawberry_growth_detection_sample.webp" + }, + { + "name": "greenhouse_crop_weed_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "crop_classification", + "location": [ + "Waldron Greenhouse, North Dakota State University, Fargo, North Dakota, United States of America" + ], + "country": "United States of America", + "environment": "greenhouse", + "real_or_synthetic": "real", + "crop_types": [ + "Blackbean", + "Canola", + "Corn", + "Field Pea", + "Flax", + "Horseweed", + "Kochia", + "Lentil", + "Palmer Amaranth", + "Ragweed", + "Redroot Pigweed", + "Soybean", + "Sugar beet", + "Waterhemp" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon EOS T7", + "Canon EOS 90D" + ], + "collection_period": "", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 28000, + "classes": [ + "Blackbean", + "Canola", + "Corn", + "Field Pea", + "Flax", + "Horseweed", + "Kochia", + "Lentil", + "Palmer Amaranth", + "Ragweed", + "Redroot Pigweed", + "Soybean", + "Sugar beet", + "Waterhemp" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2024.111097", + "citation": "G C, Sunil; Koparan, Cengiz; Upadhyay, Arjun; Ahmed, Mohammed Raju ; Zhang, Yu ; Howatt, Kirk; Sun, Xin (2024), “A Novel Automated Cloud-Based Image Datasets for High Throughput Phenotyping in Weed Identification.”, Mendeley Data, V3, doi: 10.17632/hs7d7kpd3z.3", + "zip_size_bytes": 1421843930, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/greenhouse_crop_weed_classification", + "examples_image_url": "/img/agml/sample_images/greenhouse_crop_weed_classification_sample.webp" + }, + { + "name": "greenhouse_crop_weed_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Waldron Greenhouse, North Dakota State University, Fargo, North Dakota, United States of America" + ], + "country": "United States of America", + "environment": "greenhouse", + "real_or_synthetic": "real", + "crop_types": [ + "Blackbean", + "Canola", + "Corn", + "Field Pea", + "Flax", + "Horseweed", + "Kochia", + "Lentil", + "Palmer Amaranth", + "Ragweed", + "Redroot Pigweed", + "Soybean", + "Sugar beet", + "Waterhemp" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon EOS T7", + "Canon EOS 90D" + ], + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 200, + "classes": [ + "Blackbean", + "Canola", + "Corn", + "Field Pea", + "Flax", + "Horseweed", + "Kochia", + "Lentil", + "Palmer Amaranth", + "Ragweed", + "Redroot Pigweed", + "Soybean", + "Sugar beet", + "Waterhemp" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2024.111097", + "citation": "G C, Sunil; Koparan, Cengiz; Upadhyay, Arjun; Ahmed, Mohammed Raju ; Zhang, Yu ; Howatt, Kirk; Sun, Xin (2024), “A Novel Automated Cloud-Based Image Datasets for High Throughput Phenotyping in Weed Identification.”, Mendeley Data, V3, doi: 10.17632/hs7d7kpd3z.3", + "zip_size_bytes": 2802663140, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/greenhouse_crop_weed_detection", + "examples_image_url": "/img/agml/sample_images/greenhouse_crop_weed_detection_sample.webp" + }, + { + "name": "rice_leaf_disease_classification_india", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "Sambalpur University, Odisha, India" + ], + "country": "India", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "rice" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Nikon DSLR-D5600" + ], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 5932, + "classes": [ + "Bacterialblight", + "Blast", + "Brownspot", + "Tungro" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.compag.2020.105527", + "citation": "sethy, prabira Kumar (2024), “Rice Leaf Disease Image Samples”, Mendeley Data, V2, doi: 10.17632/fwcj7stb8r.2", + "zip_size_bytes": 208012992, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/rice_leaf_disease_classification_india", + "examples_image_url": "/img/agml/sample_images/rice_leaf_disease_classification_india_sample.webp" + }, + { + "name": "three_season_weed_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "weed_detection", + "location": [ + "R. R. Foil Plant Science Research Center,Starkville, Mississppi, United States of America", + "W.B. Andrews Agricultural Systems Research Farm, Starkville, Mississippi, United States of America", + "Black Belt Experiment Station, Brooksville, Mississippi, United States of America", + "Horticultural Research and Extension Center, University of Michigan, Holt, Michigan, United States of America" + ], + "country": "United States of America", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Carpetweed", + "Eclipta", + "Goosegrass", + "Lambsquarters", + "MorningGlory", + "PalmerAmaranth", + "Purslane", + "Ragweed", + "SpottedSpurge", + "Waterhemp" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Smartphones", + "Allied Vision Camera" + ], + "collection_period": "2021, 2022, June to August 2023", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 8436, + "classes": [ + "Carpetweed", + "Eclipta", + "Goosegrass", + "Lambsquarters", + "MorningGlory", + "PalmerAmaranth", + "Purslane", + "Ragweed", + "SpottedSpurge", + "Waterhemp" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.compag.2025.110123", + "citation": "Lu, Y. (2025). 3SeasonWeedDet10: a three-season, 10-class dataset for benchmarking AI models for robust weed detection [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.14861516", + "zip_size_bytes": 48636045406, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/three_season_weed_detection", + "examples_image_url": "/img/agml/sample_images/three_season_weed_detection_sample.webp" } -] \ No newline at end of file +] diff --git a/static/img/agml/sample_images/FruitNet_quality_classification_sample.webp b/static/img/agml/sample_images/FruitNet_quality_classification_sample.webp new file mode 100644 index 0000000..7bffdb0 Binary files /dev/null and b/static/img/agml/sample_images/FruitNet_quality_classification_sample.webp differ diff --git a/static/img/agml/sample_images/crop_pest_disease_classification_sample.webp b/static/img/agml/sample_images/crop_pest_disease_classification_sample.webp new file mode 100644 index 0000000..04b1ea2 Binary files /dev/null and b/static/img/agml/sample_images/crop_pest_disease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/grapevine_development_stage_classification_sample.webp b/static/img/agml/sample_images/grapevine_development_stage_classification_sample.webp new file mode 100644 index 0000000..e63fc5b Binary files /dev/null and b/static/img/agml/sample_images/grapevine_development_stage_classification_sample.webp differ diff --git a/static/img/agml/sample_images/grapevine_growth_detection_sample.webp b/static/img/agml/sample_images/grapevine_growth_detection_sample.webp new file mode 100644 index 0000000..0175a74 Binary files /dev/null and b/static/img/agml/sample_images/grapevine_growth_detection_sample.webp differ diff --git a/static/img/agml/sample_images/greenhouse_crop_weed_classification_sample.webp b/static/img/agml/sample_images/greenhouse_crop_weed_classification_sample.webp new file mode 100644 index 0000000..cdb8a2b Binary files /dev/null and b/static/img/agml/sample_images/greenhouse_crop_weed_classification_sample.webp differ diff --git a/static/img/agml/sample_images/greenhouse_crop_weed_detection_sample.webp b/static/img/agml/sample_images/greenhouse_crop_weed_detection_sample.webp new file mode 100644 index 0000000..a547f64 Binary files /dev/null and b/static/img/agml/sample_images/greenhouse_crop_weed_detection_sample.webp differ diff --git a/static/img/agml/sample_images/rice_leaf_disease_classification_india_sample.webp b/static/img/agml/sample_images/rice_leaf_disease_classification_india_sample.webp new file mode 100644 index 0000000..155a1d8 Binary files /dev/null and b/static/img/agml/sample_images/rice_leaf_disease_classification_india_sample.webp differ diff --git a/static/img/agml/sample_images/strawberry_growth_detection_sample.webp b/static/img/agml/sample_images/strawberry_growth_detection_sample.webp new file mode 100644 index 0000000..40928a6 Binary files /dev/null and b/static/img/agml/sample_images/strawberry_growth_detection_sample.webp differ diff --git a/static/img/agml/sample_images/three_season_weed_detection_sample.webp b/static/img/agml/sample_images/three_season_weed_detection_sample.webp new file mode 100644 index 0000000..9da4700 Binary files /dev/null and b/static/img/agml/sample_images/three_season_weed_detection_sample.webp differ