js2552 · Oct 15, 2026 · Oct 15, 2026
diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json
@@ -26358,6 +26358,329 @@
     "hf_link": "https://huggingface.co/datasets/Project-AgML/uci_rice_leaf_diseases_classification",
     "examples_image_url": "/img/agml/sample_images/uci_rice_leaf_diseases_classification_sample.webp"
   },
+  {
+    "name": "PlantTom_tomato_disease_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "disease_detection",
+    "location": [],
+    "lat_lon": [],
+    "country": "",
+    "environment": "mixed",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "tomato"
+    ],
+    "sensor_modality": "rgb",
+    "imaging_equipment": [],
+    "collection_period": "",
+    "platform": "",
+    "input_data_format": "external",
+    "annotation_format": "boundingBox",
+    "classes": [
+      "Bacterial Spot",
+      "Early Blight",
+      "Late Blight",
+      "Leaf Mold",
+      "Mosaic Virus",
+      "Septoria",
+      "Spider Mites",
+      "Yellow Leaf Curl Virus"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1371/journal.pone.0349501",
+    "citation": "",
+    "bibtex": "@article{mostafa2026leafdet,\n  title={LeafDet: A lightweight and interpretable deep learning framework for tomato leaf disease detection},\n  author={Mostafa, Vaskor and Sozol, Md. Shafak Shahriar and Mondal, M. Rubaiyat Hossain},\n  journal={PLOS ONE},\n  volume={21},\n  number={5},\n  pages={e0349501},\n  year={2026},\n  publisher={Public Library of Science}\n}",
+    "data_size_bytes": 523202869,
+    "num_files": 3,
+    "num_images": 7836,
+    "source": "doi",
+    "data_link": "https://huggingface.co/datasets/vaskormostafa/PlantTom",
+    "examples_image_url": "",
+    "hosted_in_original_form": true
+  },
+  {
+    "name": "zijinshan_vegetation_segmentation",
+    "machine_learning_task": "semantic_segmentation",
+    "agricultural_task": "land_use_segmentation",
+    "location": [
+      "Zijinshan Scenic Area and surrounding urban-rural interface, Nanjing, Jiangsu Province, China"
+    ],
+    "lat_lon": [],
+    "country": "China",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [],
+    "sensor_modality": "rgb",
+    "imaging_equipment": [],
+    "collection_period": "2022-10-10, 2023-11-17, 2024-05-17",
+    "platform": "satellite",
+    "input_data_format": "external",
+    "annotation_format": "segmentationMask",
+    "classes": [
+      "background",
+      "vegetation"
+    ],
+    "license": "cc-by-nc-4.0",
+    "documentation": "https://doi.org/10.3390/rs18152487",
+    "citation": "",
+    "bibtex": "@article{hu2026comparative,\n  title={Comparative Performance Analysis of Mainstream Deep Learning and Vision Foundation Models for Small-Sample Vegetation Segmentation in High-Resolution Remote Sensing Imagery},\n  author={Hu, Le and Zhang, Fuquan},\n  journal={Remote Sensing},\n  volume={18},\n  number={15},\n  pages={2487},\n  year={2026},\n  publisher={MDPI}\n}",
+    "data_size_bytes": 300731017,
+    "num_files": 1085,
+    "num_images": 201,
+    "source": "doi",
+    "data_link": "https://huggingface.co/datasets/ledemo/zijinshan-vegetation-segmentation",
+    "examples_image_url": "",
+    "hosted_in_original_form": true
+  },
+  {
+    "name": "malabar_spinach_leaf_disease_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": [
+      "Habiganj Agricultural University, Habiganj, Bangladesh",
+      "Daffodil International University, Dhaka, Bangladesh"
+    ],
+    "lat_lon": [],
+    "country": "Bangladesh",
+    "environment": "",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "malabar spinach"
+    ],
+    "sensor_modality": "rgb",
+    "imaging_equipment": [],
+    "collection_period": "",
+    "platform": "",
+    "input_data_format": "external",
+    "annotation_format": "classLabel",
+    "classes": [
+      "Alternaria",
+      "Healthy Leaf",
+      "straw_mite"
+    ],
+    "license": "apache-2.0",
+    "documentation": "https://doi.org/10.1371/journal.pone.0340989",
+    "citation": "",
+    "bibtex": "@article{kabya2026towards,\n  title={Towards practical AI for agriculture: A self-supervised attention framework for Spinach leaf disease detection},\n  author={Kabya, Nilavro Das and Sharafat, MD Shaifullah and Emu, Rahimul Islam and Opee, Mehrab Karim and Khan, Riasat},\n  journal={PLOS ONE},\n  volume={21},\n  number={1},\n  pages={e0340989},\n  year={2026},\n  publisher={Public Library of Science}\n}",
+    "data_size_bytes": 6049522883,
+    "num_files": 4,
+    "num_images": 3435,
+    "source": "doi",
+    "data_link": "https://huggingface.co/datasets/saifullah03/malabar_spinach_leaf_disease_dataset",
+    "examples_image_url": "",
+    "hosted_in_original_form": true
+  },
+  {
+    "name": "DigitalGreen_crop_disease_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": [
+      "Bihar, India",
+      "Andhra Pradesh, India",
+      "Telangana, India",
+      "Jharkhand, India",
+      "Odisha, India",
+      "Uttar Pradesh, India",
+      "Karnataka, India",
+      "Addis Ababa, Ethiopia",
+      "Gujarat, India",
+      "Haryana, India",
+      "Kerala, India",
+      "Madhya Pradesh, India",
+      "Maharashtra, India",
+      "Rajasthan, India",
+      "West Bengal, India"
+    ],
+    "lat_lon": [],
+    "country": "India",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "aparajita",
+      "arhar",
+      "banana",
+      "barley",
+      "bean",
+      "bhindi",
+      "bitter gourd",
+      "bottle gourd",
+      "brinjal",
+      "broad bean",
+      "cabbage",
+      "canola",
+      "capsicum",
+      "carrot",
+      "cauliflower",
+      "chickpea",
+      "chikoo",
+      "chili",
+      "chilli",
+      "citrus",
+      "coriander",
+      "cotton",
+      "cowpea",
+      "crossandra",
+      "cucumber",
+      "curry leaf",
+      "custard apple",
+      "fruit trees",
+      "garlic",
+      "gram",
+      "green gram",
+      "groundnut",
+      "guava",
+      "gum",
+      "jamun",
+      "kidney bean",
+      "lemon",
+      "lemongrass",
+      "maize",
+      "mango",
+      "marigold",
+      "medicinal plants",
+      "moong",
+      "moringa",
+      "mustard",
+      "okra",
+      "onion",
+      "paddy",
+      "papaya",
+      "pea",
+      "pearl millet",
+      "peas",
+      "pepper",
+      "pigeon pea",
+      "pomegranate",
+      "potato",
+      "pumpkin",
+      "radish",
+      "rahi",
+      "red gram",
+      "rice",
+      "ridge gourd",
+      "rose",
+      "sorghum",
+      "soybean",
+      "spinach",
+      "sponge gourd",
+      "squash",
+      "strawberry",
+      "tobacco",
+      "tomato",
+      "turmeric",
+      "watermelon",
+      "wheat"
+    ],
+    "sensor_modality": "rgb",
+    "imaging_equipment": [
+      "farmer smartphones"
+    ],
+    "collection_period": "2024-12-02 to 2025-03-30",
+    "platform": "handheld",
+    "input_data_format": "external",
+    "annotation_format": "classLabel",
+    "classes": [
+      "Alternaria Blight",
+      "Anthracnose",
+      "Aphids",
+      "Armyworm",
+      "Ascochyta Blight",
+      "Bacterial Blight",
+      "Bacterial Canker",
+      "Bacterial Wilt",
+      "Banana Aphid",
+      "Banana Weevil",
+      "Banded Leaf and Sheath Blight",
+      "Bark Eating Caterpillar",
+      "Bean Beetle",
+      "Black Rot",
+      "Blast",
+      "Bollworm",
+      "Boron Deficiency",
+      "Botrytis Gray Mold",
+      "Brown Spot",
+      "Cabbage Looper",
+      "Calcium Deficiency",
+      "Cercospora Leaf Spot",
+      "Citrus Canker",
+      "Citrus Psyllid",
+      "Collar Rot",
+      "Common Scab",
+      "Cucumber Beetle",
+      "Cutworms",
+      "Diamondback Moth",
+      "Downy Mildew",
+      "Dry Root Rot",
+      "Early Blight",
+      "Fall Armyworm",
+      "Fruit Borer",
+      "Fruit Fly",
+      "Fusarium Wilt",
+      "Grain Mold",
+      "Healthy",
+      "Jassids",
+      "Late Blight",
+      "Leaf Curl Virus",
+      "Leaf Hopper",
+      "Leaf Miner",
+      "Leaf Spot",
+      "Loose Smut",
+      "Magnesium Deficiency",
+      "Mango Hopper",
+      "Mealybugs",
+      "Melanose",
+      "Mites",
+      "Mosaic Virus",
+      "Nitrogen Deficiency",
+      "Onion Maggot",
+      "Panama Wilt",
+      "Papaya Mealybug",
+      "Papaya Ring Spot Virus",
+      "Phomopsis Blight",
+      "Phosphorus Deficiency",
+      "Phytophthora Blight",
+      "Pod Borer",
+      "Potassium Deficiency",
+      "Potato Tuber Moth",
+      "Powdery Mildew",
+      "Purple Blotch",
+      "Red Pumpkin Beetle",
+      "Rice Bug",
+      "Root Rot",
+      "Rust",
+      "Scab",
+      "Scale Insects",
+      "Sheath Blight",
+      "Shoot and Fruit Borer",
+      "Sigatoka Leaf Spot",
+      "Smut",
+      "Sooty Mold",
+      "Spider Mites",
+      "Squash Bug",
+      "Stem Borer",
+      "Stemphylium Blight",
+      "Sterility Mosaic",
+      "Stripe Rust",
+      "Thrips",
+      "Tobacco Caterpillar",
+      "Tobacco Mosaic Virus",
+      "Turcicum Leaf Blight",
+      "White Rust",
+      "Whiteflies",
+      "Wilt"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://huggingface.co/datasets/DigiGreen/Crop_Disease_Images",
+    "citation": "Digital Green. (2026). Crop Disease Expert Annotations [Dataset]. Hugging Face. https://huggingface.co/datasets/DigiGreen/Crop_Disease_Images",
+    "bibtex": "@dataset{digigreen_crop_disease_2026,\n  title={Crop Disease Expert Annotations},\n  author={Digital Green},\n  year={2026},\n  url={https://huggingface.co/datasets/DigiGreen/Crop_Disease_Images},\n  license={CC-BY-4.0}\n}",
+    "data_size_bytes": 183107473,
+    "num_files": 993,
+    "num_images": 989,
+    "source": "doi",
+    "data_link": "https://huggingface.co/datasets/DigiGreen/Crop_Disease_Images",
+    "examples_image_url": "",
+    "hosted_in_original_form": true
+  },
   {
     "name": "image_safari_minor_sites",
     "machine_learning_task": "unlabeled",