diff --git a/static/data/datasets.json b/static/data/datasets.json index 14f1ca1..31d97a9 100644 --- a/static/data/datasets.json +++ b/static/data/datasets.json @@ -216,37 +216,6 @@ "apple" ] }, - { - "name": "arabica_coffee_leaf_disease_classification", - "machine_learning_task": "image_classification", - "agricultural_task": "arabica_coffee_leaf_disease_classification", - "location": "Kenya, Africa", - "sensor_modality": "RGB", - "real_or_synthetic": "real", - "platform": "uav", - "input_data_format": "jpg, jpeg", - "annotation_format": "directory_names", - "num_images": 58549, - "documentation": "https://www.sciencedirect.com/science/article/pii/S2352340921004261?via%3Dihub#sec0001", - "classes": "Cerscospora, Healthy, Leaf_rust, Miner, Phoma", - "stats_mean": [ - 0.466, - 0.613, - 0.405 - ], - "stats_std": [ - 0.132, - 0.101, - 0.139 - ], - "examples_image_url": "/img/agml/sample_images/arabica_coffee_leaf_disease_classification_examples.webp", - "source": "agml", - "license": "CC BY 4.0", - "citation": "@article{JEPKOECH2021107142, title = {Arabica coffee leaf images dataset for coffee leaf disease detection and classification}, journal = {Data in Brief}, volume = {36}, pages = {107142}, year = {2021}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2021.107142}, url = {https://www.sciencedirect.com/science/article/pii/S2352340921004261}, author = {Jennifer Jepkoech and David Muchangi Mugo and Benson K. Kenduiywo and Edna Chebet Too}}", - "crop_types": [ - "coffee" - ] - }, { "name": "autonomous_greenhouse_regression", "machine_learning_task": null, @@ -270,68 +239,6 @@ "lettuce" ] }, - { - "name": "banana_leaf_disease_classification", - "machine_learning_task": "image_classification", - "agricultural_task": "banana_leaf_disease_classification", - "location": "Ethiopia, Africa", - "sensor_modality": "RGB", - "real_or_synthetic": "real", - "platform": "uav", - "input_data_format": "JPG", - "annotation_format": "directory_names", - "num_images": 1288, - "documentation": "https://www.researchgate.net/publication/380900090_Sigatoka_and_Xanthomonas_Banana_Leaf_Disease_Detection_Via_Transfer_Learning", - "classes": "healthy, segatoka, xamthomonas", - "stats_mean": [ - 0.44, - 0.479, - 0.23 - ], - "stats_std": [ - 0.205, - 0.204, - 0.186 - ], - "examples_image_url": "/img/agml/sample_images/banana_leaf_disease_classification_examples.webp", - "source": "agml", - "license": "CC BY 4.0", - "citation": "hailu, yordanos (2021), “Banana Leaf Disease Images”, Mendeley Data, V1, doi: 10.17632/rjykr62kdh.1", - "crop_types": [ - "banana" - ] - }, - { - "name": "bean_disease_uganda", - "machine_learning_task": "image_classification", - "agricultural_task": "disease_classification", - "location": "Uganda, Africa", - "sensor_modality": "RGB", - "real_or_synthetic": "real", - "platform": "handheld", - "input_data_format": "JPG", - "annotation_format": "directory_names", - "num_images": 1295, - "documentation": "https://github.com/AI-Lab-Makerere/ibean/", - "classes": "angular_leaf_spot, bean_rust, healthy", - "stats_mean": [ - 0.485, - 0.519, - 0.311 - ], - "stats_std": [ - 0.182, - 0.199, - 0.169 - ], - "examples_image_url": "/img/agml/sample_images/bean_disease_uganda_examples.webp", - "source": "agml", - "license": "MIT", - "citation": "", - "crop_types": [ - "bean" - ] - }, { "name": "bean_synthetic_earlygrowth_aerial", "machine_learning_task": "semantic_segmentation", @@ -386,29 +293,6 @@ "betel" ] }, - { - "name": "blackgram_plant_leaf_disease_classification", - "machine_learning_task": "image_classification", - "agricultural_task": "blackgram_plant_leaf_disease_classification", - "location": "India, Asia", - "sensor_modality": "RGB", - "real_or_synthetic": "real", - "platform": "uav", - "input_data_format": "JPG", - "annotation_format": "directory_names", - "num_images": 1007, - "documentation": "https://www.sciencedirect.com/science/article/pii/S2352340922009295", - "classes": "anthracnose, healthy, leaf_crinckle, powdery_mildew, yellow_mosaic", - "stats_mean": null, - "stats_std": null, - "examples_image_url": "/img/agml/sample_images/blackgram_plant_leaf_disease_classification_examples.webp", - "source": "agml", - "license": "CC BY 4.0", - "citation": "Talasila, Srinivas; Rawal, Kirti; Sethi, Gaurav; MSS, Sanjay; M, Surya Prakash Reddy (2022), “Blackgram Plant Leaf Disease Dataset”, Mendeley Data, V3, doi: 10.17632/zfcv9fmrgv.3", - "crop_types": [ - "blackgram" - ] - }, { "name": "carrot_weeds_germany", "machine_learning_task": "semantic_segmentation", @@ -440,37 +324,6 @@ "carrot" ] }, - { - "name": "chilli_leaf_classification", - "machine_learning_task": "image_classification", - "agricultural_task": "chilli_leaf_classification", - "location": "India, Asia", - "sensor_modality": "RGB", - "real_or_synthetic": "real", - "platform": "uav", - "input_data_format": "JPG", - "annotation_format": "directory_names", - "num_images": 10974, - "documentation": "https://www.researchgate.net/publication/380611658_Dataset_of_Chilli_and_Onion_Plant_Leaf_Images_for_Classification_and_Detection", - "classes": "cercospora, healthy, mites_and_trips, nutritional, powdery_mildew", - "stats_mean": [ - 0.54, - 0.562, - 0.4 - ], - "stats_std": [ - 0.178, - 0.177, - 0.182 - ], - "examples_image_url": "/img/agml/sample_images/chilli_leaf_classification_examples.webp", - "source": "agml", - "license": "CC BY 4.0", - "citation": "Aishwarya, M.P & Reddy, A.. (2024). Dataset of Chilli and Onion Plant Leaf Images for Classification and Detection. Data in Brief. 54. 110524. 10.1016/j.dib.2024.110524. ", - "crop_types": [ - "chilli" - ] - }, { "name": "coconut_tree_disease_classification", "machine_learning_task": "image_classification", diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index 2034be7..cba846e 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -2579,5 +2579,209 @@ "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/potato_leaf_blight_classification", "examples_image_url": "/img/agml/sample_images/potato_leaf_blight_classification_sample.webp" + }, + { + "name": "fruit_leaf_variety_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "variety_classification", + "location": "Bangladesh", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "Aegle marmelos", + "Black plum", + "Custard Apple", + "Guava", + "Jackfruit", + "Lotkon", + "Lychee", + "Mango", + "Plum", + "Star Fruit" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 3173, + "classes": [ + "Aegle marmelos", + "Black plum", + "Custard Apple", + "Guava", + "Jackfruit", + "Lotkon", + "Lychee", + "Mango", + "Plum", + "Star Fruit" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2025.111879", + "citation": "Abedin, Minhajul ; Islam, Md. Sujon ; Sultana, Dr. Naznin (2025), “Multi-Class Fruit Leaf Classification Dataset (10 Classes)”, Mendeley Data, V2, doi: 10.17632/4gxzx6h7gv.2", + "zip_size_bytes": 10452246264, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/fruit_leaf_variety_classification", + "examples_image_url": "/img/agml/sample_images/fruit_leaf_variety_classification_sample.png" + }, + { + "name": "tropical_flower_variety_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "variety_classification", + "location": "Bangladesh", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "Bougainvillea", + "Crown of thorns", + "Hibiscus", + "Jungle geranium", + "Madagascar periwinkle", + "Marigold", + "Rose" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 4319, + "classes": [ + "Bougainvillea", + "Crown of thorns", + "Hibiscus", + "Jungle geranium", + "Madagascar periwinkle", + "Marigold", + "Rose" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2025.111374", + "citation": "Rahat, Riazul Islam; hossain, Md.Sohag; Mojumdar, Mayen Uddin ; Chakraborty, Narayan Ranjan; Noori, Sheak Rashed Haider; Siddiquee, Shah Md Tanvir (2024), “Tropical Flower Dataset: Seven Species from Bangladesh for Classification and Ecological Research.”, Mendeley Data, V1, doi: 10.17632/njfg9nh92t.1", + "zip_size_bytes": 1666250266, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/tropical_flower_variety_classification", + "examples_image_url": "/img/agml/sample_images/tropical_flower_variety_classification_sample.png" + }, + { + "name": "arabica_coffee_leaf_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": "Kenya, Africa", + "sensor_modality": "RGB", + "real_or_synthetic": "real", + "platform": "uav", + "environment": "field", + "input_data_format": "jpg, jpeg", + "annotation_format": "classLabel", + "num_images": 58549, + "documentation": "https://doi.org/10.1016/j.dib.2021.107142", + "classes": ["Cerscospora, Healthy, Leaf_rust, Miner, Phoma"], + "stats_mean": [ + 0.466, + 0.613, + 0.405 + ], + "stats_std": [ + 0.132, + 0.101, + 0.139 + ], + "examples_image_url": "/img/agml/sample_images/arabica_coffee_leaf_disease_classification_sample.png", + "source": "huggingface", + "license": "cc-by-4.0", + "citation": "Jepkoech, jennifer; Kenduiywo, Benson; Mugo, David; Chebet, Edna (2021), “JMuBEN”, Mendeley Data, V1, doi: 10.17632/t2r6rszp5c.1", + "crop_types": [ + "coffee" + ], + "hf_link": "https://huggingface.co/datasets/Project-AgML/arabica_coffee_leaf_disease_classification" + }, + { + "name": "banana_leaf_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": "Ethiopia, Africa", + "sensor_modality": "RGB", + "real_or_synthetic": "real", + "platform": "uav", + "environment": "field", + "input_data_format": "JPG", + "annotation_format": "classLabel", + "num_images": 1288, + "documentation": "https://www.researchgate.net/publication/380900090_Sigatoka_and_Xanthomonas_Banana_Leaf_Disease_Detection_Via_Transfer_Learning", + "classes": "healthy, segatoka, xamthomonas", + "stats_mean": [ + 0.44, + 0.479, + 0.23 + ], + "stats_std": [ + 0.205, + 0.204, + 0.186 + ], + "examples_image_url": "/img/agml/sample_images/banana_leaf_disease_classification_examples.webp", + "source": "agml", + "license": "CC BY 4.0", + "citation": "hailu, yordanos (2021), “Banana Leaf Disease Images”, Mendeley Data, V1, doi: 10.17632/rjykr62kdh.1", + "crop_types": [ + "banana" + ], + "hf_link": "https://huggingface.co/datasets/Project-AgML/banana_leaf_disease_classification" + }, + { + "name": "bean_disease_uganda", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": "Uganda, Africa", + "sensor_modality": "RGB", + "real_or_synthetic": "real", + "platform": "handheld", + "environment": "Field", + "input_data_format": "JPG", + "annotation_format": "classLabel", + "num_images": 1295, + "documentation": "https://github.com/AI-Lab-Makerere/ibean/", + "classes": "angular_leaf_spot, bean_rust, healthy", + "stats_mean": [ + 0.485, + 0.519, + 0.311 + ], + "stats_std": [ + 0.182, + 0.199, + 0.169 + ], + "examples_image_url": "/img/agml/sample_images/bean_disease_uganda_examples.webp", + "source": "agml", + "license": "MIT", + "citation": "", + "crop_types": [ + "bean" + ], + "hf_link": "https://huggingface.co/datasets/Project-AgML/bean_disease_uganda" + }, + { + "name": "blackgram_plant_leaf_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": "India, Asia", + "sensor_modality": "RGB", + "real_or_synthetic": "real", + "platform": "uav", + "environment": "Field", + "input_data_format": "JPG", + "annotation_format": "classLabel", + "num_images": 1007, + "documentation": "https://doi.org/10.1016/j.dib.2022.108725", + "classes": ["anthracnose, healthy, leaf_crinckle, powdery_mildew, yellow_mosaic"], + "stats_mean": null, + "stats_std": null, + "examples_image_url": "/img/agml/sample_images/blackgram_plant_leaf_disease_classification_examples.webp", + "source": "agml", + "license": "CC BY 4.0", + "citation": "Talasila, Srinivas; Rawal, Kirti; Sethi, Gaurav; MSS, Sanjay; M, Surya Prakash Reddy (2022), “Blackgram Plant Leaf Disease Dataset”, Mendeley Data, V3, doi: 10.17632/zfcv9fmrgv.3", + "crop_types": [ + "blackgram" + ], + "hf_link": "https://huggingface.co/datasets/Project-AgML/blackgram_plant_leaf_disease_classification" } ] diff --git a/static/img/agml/sample_images/fruit_leaf_variety_classification_sample.png b/static/img/agml/sample_images/fruit_leaf_variety_classification_sample.png new file mode 100644 index 0000000..f39baf2 Binary files /dev/null and b/static/img/agml/sample_images/fruit_leaf_variety_classification_sample.png differ diff --git a/static/img/agml/sample_images/tropical_flower_variety_classification_sample.png b/static/img/agml/sample_images/tropical_flower_variety_classification_sample.png new file mode 100644 index 0000000..e861a72 Binary files /dev/null and b/static/img/agml/sample_images/tropical_flower_variety_classification_sample.png differ