new datasets

static/data/hf_datasets.json

@@ -5768,5 +5768,282 @@
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/rice_panicle_detection",
     "examples_image_url": "/img/agml/sample_images/rice_panicle_detection_sample.webp"
+  },
+  {
+    "name": "date_palm_leaf_disease_classification_iraq",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Iraq",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "date palm"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 3000,
+    "classes": [
+      "Bug",
+      "Dubas",
+      "Healthy",
+      "Honey"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2023.109371",
+    "citation": "Mazin, Abdullah; Almayaly, Haider (2023), “Image dataset of infected date palm leaves by dubas insects”, Mendeley Data, V2, doi: 10.17632/2nh364p2bc.2",
+    "zip_size_bytes": 984229588,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/date_palm_leaf_disease_classification_iraq",
+    "examples_image_url": "/img/agml/sample_images/date_palm_leaf_disease_classification_iraq_sample.webp"
+  },
+  {
+    "name": "BananaLSD_leaf_disease_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Bangladesh",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "banana"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 937,
+    "augmented_num_images": 1600,
+    "augmented_zip_size_bytes": 27508683,
+    "classes": [
+      "cordana",
+      "healthy",
+      "pestalotiopsis",
+      "sigatoka"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2023.109608",
+    "citation": "E Arman, Shifat; Baki Bhuiyan, Md Abdullahil; Abdullah, Hasan Muhammad; Islam, Shariful; Chowdhury, Tahsin Tanha; Hossain, Md. Arban (2023), “Banana Leaf Spot Diseases (BananaLSD) Dataset for Classification of Banana Leaf Diseases Using Machine Learning”, Mendeley Data, V1, doi: 10.17632/9tb7k297ff.1",
+    "zip_size_bytes": 28172016,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/BananaLSD_leaf_disease_classification",
+    "examples_image_url": "/img/agml/sample_images/BananaLSD_leaf_disease_classification_sample.webp"
+  },
+  {
+    "name": "groundnut_leaf_disease_classification_2",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "India",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "groundnut"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 3058,
+    "classes": [
+      "early_leaf_spot",
+      "healthy leaf",
+      "late leaf spot",
+      "nutrition deficiency",
+      "rust"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2023.109185",
+    "citation": "Manvikar, Aishwarya; Reddy, Padmanabha  (2023), “Dataset of groundnut plant leaf images for classification and detection”, Mendeley Data, V3, doi: 10.17632/22p2vcbxfk.3",
+    "zip_size_bytes": 809924529,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/groundnut_leaf_disease_classification_2",
+    "examples_image_url": "/img/agml/sample_images/groundnut_leaf_disease_classification_2_sample.webp"
+  },
+  {
+    "name": "DIMPSAR_medicinal_plant_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "variety_classification",
+    "location": "India",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 5945,
+    "classes": [
+      "Aloevera",
+      "Amla",
+      "Amruta_Balli",
+      "Arali",
+      "Ashoka",
+      "Ashwagandha",
+      "Avacado",
+      "Bamboo",
+      "Basale",
+      "Betel",
+      "Betel_Nut",
+      "Brahmi",
+      "Castor",
+      "Curry_Leaf",
+      "Doddapatre",
+      "Ekka",
+      "Ganike",
+      "Gauva",
+      "Geranium",
+      "Henna",
+      "Hibiscus",
+      "Honge",
+      "Insulin",
+      "Jasmine",
+      "Lemon",
+      "Lemon_grass",
+      "Mango",
+      "Mint",
+      "Nagadali",
+      "Neem",
+      "Nithyapushpa",
+      "Nooni",
+      "Pappaya",
+      "Pepper",
+      "Pomegranate",
+      "Raktachandini",
+      "Rose",
+      "Sapota",
+      "Tulasi",
+      "Wood_sorel"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2023.109388",
+    "citation": "B R, Pushpa; Rani, Shobha (2023), “Indian Medicinal Leaves Image Datasets”, Mendeley Data, V3, doi: 10.17632/748f8jkphb.3",
+    "zip_size_bytes": 264965896,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/DIMPSAR_medicinal_plant_classification",
+    "examples_image_url": "/img/agml/sample_images/DIMPSAR_medicinal_plant_classification_sample.webp"
+  },
+  {
+    "name": "DIMPSAR_medicinal_leaf_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "variety_classification",
+    "location": "India",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 6900,
+    "classes": [
+      "Aloevera",
+      "Amla",
+      "Amruthaballi",
+      "Arali",
+      "Astma_weed",
+      "Badipala",
+      "Balloon_Vine",
+      "Bamboo",
+      "Beans",
+      "Betel",
+      "Bhrami",
+      "Bringaraja",
+      "Caricature",
+      "Castor",
+      "Catharanthus",
+      "Chakte",
+      "Chilly",
+      "Citron lime (herelikai)",
+      "Coffee",
+      "Common rue(naagdalli)",
+      "Coriender",
+      "Curry",
+      "Doddpathre",
+      "Drumstick",
+      "Ekka",
+      "Eucalyptus",
+      "Ganigale",
+      "Ganike",
+      "Gasagase",
+      "Ginger",
+      "Globe Amarnath",
+      "Guava",
+      "Henna",
+      "Hibiscus",
+      "Honge",
+      "Insulin",
+      "Jackfruit",
+      "Jasmine",
+      "Kambajala",
+      "Kasambruga",
+      "Kohlrabi",
+      "Lantana",
+      "Lemon",
+      "Lemongrass",
+      "Malabar_Nut",
+      "Malabar_Spinach",
+      "Mango",
+      "Marigold",
+      "Mint",
+      "Neem",
+      "Nelavembu",
+      "Nerale",
+      "Nooni",
+      "Onion",
+      "Padri",
+      "Palak(Spinach)",
+      "Papaya",
+      "Parijatha",
+      "Pea",
+      "Pepper",
+      "Pomoegranate",
+      "Pumpkin",
+      "Raddish",
+      "Rose",
+      "Sampige",
+      "Sapota",
+      "Seethaashoka",
+      "Seethapala",
+      "Spinach1",
+      "Tamarind",
+      "Taro",
+      "Tecoma",
+      "Thumbe",
+      "Tomato",
+      "Tulsi",
+      "Turmeric",
+      "ashoka",
+      "camphor",
+      "kamakasturi",
+      "kepala"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2023.109388",
+    "citation": "B R, Pushpa; Rani, Shobha (2023), “Indian Medicinal Leaves Image Datasets”, Mendeley Data, V3, doi: 10.17632/748f8jkphb.3",
+    "zip_size_bytes": 9484095068,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/DIMPSAR_medicinal_leaf_classification",
+    "examples_image_url": "/img/agml/sample_images/DIMPSAR_medicinal_leaf_classification_sample.webp"
+  },
+  {
+    "name": "SoyNet_leaf_health_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "health_classification",
+    "location": "India",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "soybean"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 3655,
+    "classes": [
+      "Disease",
+      "Healthy"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2023.109447",
+    "citation": "Rajput, Arpan Singh ; Rajput, Alpa Singh; Shukla, Shailja; Thakur, S S (2026), “SoyNet: Indian Soybean Image dataset with quality images captured from the agriculture field ( healthy and disease Images)”, Mendeley Data, V3, doi: 10.17632/w2r855hpx8.3",
+    "zip_size_bytes": 9963834087,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/SoyNet_leaf_health_classification",
+    "examples_image_url": "/img/agml/sample_images/SoyNet_leaf_health_classification_sample.webp"
   }
 ]