diff --git a/docs/credits.mdx b/docs/credits.mdx
index 6ed706e..c634e90 100644
--- a/docs/credits.mdx
+++ b/docs/credits.mdx
@@ -22,6 +22,8 @@ dependencies from the AgML project configuration.
 | dict2xml | >=1.7.6 |
 | ipywidgets | >=8.1.5 |
 | rich | >=14.0.0 |
+| datasets | * |
+| transformers | * |
 
 ## Development dependencies
 
@@ -40,21 +42,4 @@ dependencies from the AgML project configuration.
 | coverage | >=7.6.1 |
 | interrogate | >=1.7.0 |
 
-## Docs dependencies
-
-| Project | Version |
-| --- | --- |
-| mkdocs | >=1.5.0 |
-| mkdocs-material | >=9.5.0 |
-| markdown-callouts | >=0.4.0 |
-| markdown-exec | >=1.7.0 |
-| mkdocs-gen-files | >=0.5.0 |
-| mkdocs-literate-nav | >=0.6.0 |
-| mkdocs-coverage | >=1.0.0 |
-| mkdocstrings | >=0.24.0 |
-| mkdocstrings-python | >=1.8.0 |
-| mkdocs-git-revision-date-localized-plugin | >=1.2.0 |
-| mkdocs-minify-plugin | >=0.8.0 |
-
-For the fully generated credits (including resolved versions and licenses), refer to the
-upstream generator in the AgML repository at `scripts/docs/gen_credits.py`.
+For the full dependency list with resolved versions and licenses, see [pyproject.toml](https://github.com/Project-AgML/AgML/blob/main/pyproject.toml) in the AgML repository.
diff --git a/docs/index.mdx b/docs/index.mdx
index c941e3a..a167212 100644
--- a/docs/index.mdx
+++ b/docs/index.mdx
@@ -108,6 +108,10 @@ train_ds.as_torch_dataset()
 
 You're now ready to use AgML for training your own models! Luckily, AgML comes with a training module that enables quick-start training of standard deep learning models on agricultural datasets. Training a grape detection model is as simple as the following code:
 
+:::warning Deprecation Notice
+`agml.models` is deprecated and will be removed in a future release. The example below still works but will emit a `DeprecationWarning` at import time.
+:::
+
 ```python
 import agml
 import agml.models
@@ -126,6 +130,29 @@ model = agml.models.DetectionModel(num_classes=loader.num_classes)
 model.run_training(loader)
 ```
 
+## HuggingFace Integration
+
+AgML now includes a `HuggingFaceDataLoader` for loading datasets directly from the [Hugging Face Hub](https://huggingface.co/datasets) into native HuggingFace `DatasetDict` objects, compatible with `transformers` and `diffusers` pipelines.
+
+```python
+from agml.data import HuggingFaceDataLoader
+
+# Load a dataset from the Hub
+loader = HuggingFaceDataLoader("org/dataset-name")
+
+# Load a specific config/subset
+loader = HuggingFaceDataLoader("org/dataset-name", config="augmented")
+
+# Split into train/val/test with optional stratification
+dataset = loader.split(val_size=0.1, test_size=0.1, stratify_cols="label")
+# Returns a DatasetDict with 'train', 'val', and 'test' splits
+
+# Access the underlying DatasetDict at any time
+dataset = loader.dataset
+```
+
+The `HuggingFaceDataLoader` automatically casts image-like columns to the HuggingFace `Image` type for decoded pixel access. Stratified splitting is supported across one or more columns using a composite key.
+
 ## Public Datasets
 
 AgML contains a wide variety of public datasets from various locations across the world:
diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json
index 1b0d5c5..d9daa1f 100644
--- a/static/data/hf_datasets.json
+++ b/static/data/hf_datasets.json
@@ -19,7 +19,7 @@
     "documentation": "wGrapeUNIPD-DL_white_grape_bunch_detection",
     "stats_mean": null,
     "stats_std": null,
-    "examples_image_url": null,
+    "examples_image_url": "/img/agml/sample_images/wGrapeUNIPD-DL_white_grape_bunch_detection_sample.png",
     "license": "cc-by-4.0",
     "citation": null,
     "parent_dataset": null,
@@ -53,7 +53,7 @@
     ],
     "stats_mean": null,
     "stats_std": null,
-    "examples_image_url": null,
+    "examples_image_url": "/img/agml/sample_images/Strawberry-DS_strawberry_detection_sample.png",
     "license": null,
     "citation": null,
     "parent_dataset": null,
@@ -81,7 +81,7 @@
     ],
     "stats_mean": null,
     "stats_std": null,
-    "examples_image_url": null,
+    "examples_image_url": "/img/agml/sample_images/synthetic_cowpea_flower_detection_sample.png",
     "license": null,
     "citation": null,
     "parent_dataset": null,
@@ -110,7 +110,7 @@
     ],
     "stats_mean": null,
     "stats_std": null,
-    "examples_image_url": null,
+    "examples_image_url": "/img/agml/sample_images/synthetic_cowpea_pod_detection_sample.png",
     "license": null,
     "citation": null,
     "parent_dataset": null,
@@ -139,7 +139,7 @@
     ],
     "stats_mean": null,
     "stats_std": null,
-    "examples_image_url": null,
+    "examples_image_url": "/img/agml/sample_images/GEMINI_cowpea_pod_detection_sample.png",
     "license": null,
     "citation": null,
     "parent_dataset": null,
@@ -167,7 +167,7 @@
     ],
     "stats_mean": null,
     "stats_std": null,
-    "examples_image_url": null,
+    "examples_image_url": "/img/agml/sample_images/GEMINI_cowpea_flower_detection_sample.png",
     "license": null,
     "citation": null,
     "parent_dataset": null,
@@ -200,7 +200,7 @@
     "examples_image_url": "/img/agml/sample_images/cotton_leaf_disease_classification_sample.png",
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.112142",
-    "citation": "Ripon, Shamim; Gani , Raiyan ; Niha, Nazratan Mazumder ; Rahat, Wasimul Bari ; Toufiq, Shafaeat Hasan ; Maisha, Mushfida Ferdous ; Ahmed, Jubaer  (2025), “Cotton Leaf Image Dataset for Disease Classification ”, Mendeley Data, V1, doi: 10.17632/t9hgvk2h9p.1",
+    "citation": "Ripon, Shamim; Gani , Raiyan ; Niha, Nazratan Mazumder ; Rahat, Wasimul Bari ; Toufiq, Shafaeat Hasan ; Maisha, Mushfida Ferdous ; Ahmed, Jubaer  (2025), \u201cCotton Leaf Image Dataset for Disease Classification \u201d, Mendeley Data, V1, doi: 10.17632/t9hgvk2h9p.1",
     "zip_size_bytes": 61300000,
     "augmented_zip_size_bytes": 359300000,
     "source": "huggingface",
@@ -236,7 +236,7 @@
     "examples_image_url": "/img/agml/sample_images/eggplant_leaf_disease_classification_sample.png",
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.112140",
-    "citation": "Nirob, Md Asraful Sharker; Bishshash, Prayma; Ayan, Mariyam Bin; Khatun, Tania; Uddin, Mohammad Shorif (2024), “Eggplant Dataset: A Comprehensive Dataset for Agricultural Research and Disease Detection”, Mendeley Data, V1, doi: 10.17632/5drkk544k8.1",
+    "citation": "Nirob, Md Asraful Sharker; Bishshash, Prayma; Ayan, Mariyam Bin; Khatun, Tania; Uddin, Mohammad Shorif (2024), \u201cEggplant Dataset: A Comprehensive Dataset for Agricultural Research and Disease Detection\u201d, Mendeley Data, V1, doi: 10.17632/5drkk544k8.1",
     "zip_size_bytes": 499400000,
     "augmented_zip_size_bytes": 767800000,
     "source": "huggingface",
@@ -265,11 +265,12 @@
     ],
     "license": "cc-by-nc-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.112174",
-    "citation": "Khan, Abid; Preanto, Sabit Ahamed; Paul, Tapon; Bijoy, Md Hasan Imam (2025), “MoringaLeafNet: A Multi-Class Leaf Disease Dataset for Precision Agriculture and Deep Learning Research”, Mendeley Data, V5, doi: 10.17632/w8sr775pjb.5",
+    "citation": "Khan, Abid; Preanto, Sabit Ahamed; Paul, Tapon; Bijoy, Md Hasan Imam (2025), \u201cMoringaLeafNet: A Multi-Class Leaf Disease Dataset for Precision Agriculture and Deep Learning Research\u201d, Mendeley Data, V5, doi: 10.17632/w8sr775pjb.5",
     "zip_size_bytes": 1130000000,
     "augmented_zip_size_bytes": 2860000000,
     "source": "huggingface",
-    "hf_link": "https://huggingface.co/datasets/Project-AgML/MoringaLeafNet_disease_classification"
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/MoringaLeafNet_disease_classification",
+    "examples_image_url": "/img/agml/sample_images/MoringaLeafNet_disease_classification_sample.png"
   },
   {
     "name": "ash_gourd_disease_classification",
@@ -294,10 +295,11 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111997",
-    "citation": "Jahan, Nusrat; Hasan, Md Zahid (2024), “Ash Gourd Leaf Healthy and Disease Dataset”, Mendeley Data, V2, doi: 10.17632/zj4th6xvdp.2",
+    "citation": "Jahan, Nusrat; Hasan, Md Zahid (2024), \u201cAsh Gourd Leaf Healthy and Disease Dataset\u201d, Mendeley Data, V2, doi: 10.17632/zj4th6xvdp.2",
     "zip_size_bytes": 1170000000,
     "source": "huggingface",
-    "hf_link": "https://huggingface.co/datasets/Project-AgML/ash_gourd_disease_classification"
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/ash_gourd_disease_classification",
+    "examples_image_url": "/img/agml/sample_images/ash_gourd_disease_classification_sample.png"
   },
   {
     "name": "jujube_bruise_classification",
@@ -320,11 +322,12 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.112031",
-    "citation": "Tabib, Md Arham; Liza, Sumyia Sabrin; Rahman, Md Mizanur (2025), “JujubeBruiseNet: A Dataset for Bruise Detection in Ziziphus mauritiana”, Mendeley Data, V5, doi: 10.17632/3mtdhrwgfr.5",
+    "citation": "Tabib, Md Arham; Liza, Sumyia Sabrin; Rahman, Md Mizanur (2025), \u201cJujubeBruiseNet: A Dataset for Bruise Detection in Ziziphus mauritiana\u201d, Mendeley Data, V5, doi: 10.17632/3mtdhrwgfr.5",
     "zip_size_bytes": 21700000,
     "augmented_zip_size_bytes": 162900000,
     "source": "huggingface",
-    "hf_link": "https://huggingface.co/datasets/Project-AgML/jujube_bruise_classification"
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/jujube_bruise_classification",
+    "examples_image_url": "/img/agml/sample_images/jujube_bruise_classification_sample.png"
   },
   {
     "name": "MangoImageBD_classification",
@@ -360,11 +363,12 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111908",
-    "citation": "Ferdaus, Md Hasanul; Prito, Rizvee Hassan; Ahmed, Masud ; Rahoman, Md Mizanur; Islam, Mohammad Manzurul; Ali, Sawkat; Islam, Maheen; Rasel, Ahmed Abdal Shafi; Rahman, Md. Atiqur; Jabid, Taskeed; Ibrahim, Muhammad (2024), “MangoImageBD: An Extensive Image Dataset of Common and Popular Mango Varieties in Bangladesh for Identification and Classification”, Mendeley Data, V2, doi: 10.17632/hp2cdckpdr.2",
+    "citation": "Ferdaus, Md Hasanul; Prito, Rizvee Hassan; Ahmed, Masud ; Rahoman, Md Mizanur; Islam, Mohammad Manzurul; Ali, Sawkat; Islam, Maheen; Rasel, Ahmed Abdal Shafi; Rahman, Md. Atiqur; Jabid, Taskeed; Ibrahim, Muhammad (2024), \u201cMangoImageBD: An Extensive Image Dataset of Common and Popular Mango Varieties in Bangladesh for Identification and Classification\u201d, Mendeley Data, V2, doi: 10.17632/hp2cdckpdr.2",
     "zip_size_bytes": 163600000,
     "augmented_zip_size_bytes": 1140000000,
     "source": "huggingface",
-    "hf_link": "https://huggingface.co/datasets/Project-AgML/MangoImageBD_classification"
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/MangoImageBD_classification",
+    "examples_image_url": "/img/agml/sample_images/MangoImageBD_classification_sample.png"
   },
   {
     "name": "okra_maturity_classification",
@@ -387,10 +391,11 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111982",
-    "citation": "Bharathi, Nikhilesh Kumar; FJ, Ferbin ; Sivapatham, Shoba (2025), “Okra Image Dataset”, Mendeley Data, V1, doi: 10.17632/jmhz4826f2.1",
+    "citation": "Bharathi, Nikhilesh Kumar; FJ, Ferbin ; Sivapatham, Shoba (2025), \u201cOkra Image Dataset\u201d, Mendeley Data, V1, doi: 10.17632/jmhz4826f2.1",
     "zip_size_bytes": 62600000,
     "source": "huggingface",
-    "hf_link": "https://huggingface.co/datasets/Project-AgML/okra_maturity_classification"
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/okra_maturity_classification",
+    "examples_image_url": "/img/agml/sample_images/okra_maturity_classification_sample.png"
   },
   {
     "name": "pomegranate_disease_classification",
@@ -417,7 +422,8 @@
     "citation": "Mohammed, B. A., Abdalla, P. A., & Hassan, H. O. (2025). Halabja Pomegranate Fruit Disease Image Dataset. Zenodo. https://doi.org/10.5281/zenodo.15856012",
     "zip_size_bytes": 778600000,
     "source": "huggingface",
-    "hf_link": "https://huggingface.co/datasets/Project-AgML/pomegranate_disease_classification"
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/pomegranate_disease_classification",
+    "examples_image_url": "/img/agml/sample_images/pomegranate_disease_classification_sample.png"
   },
   {
     "name": "rice_disease_classification_bangladesh",
@@ -446,7 +452,7 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111977",
-    "citation": "Rifat, Shakhawath Hossain; Layes, Tanvir Almas; Hasan, Afif; Mojumdar, Mayen Uddin  (2024), “Rice Leaf Disease and Pest Dataset Overview”, Mendeley Data, V1, doi: 10.17632/vwv3nry3wr.1",
+    "citation": "Rifat, Shakhawath Hossain; Layes, Tanvir Almas; Hasan, Afif; Mojumdar, Mayen Uddin  (2024), \u201cRice Leaf Disease and Pest Dataset Overview\u201d, Mendeley Data, V1, doi: 10.17632/vwv3nry3wr.1",
     "zip_size_bytes": 550444671,
     "augmented_zip_size_bytes": 2828265966,
     "source": "huggingface",
@@ -460,7 +466,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["rose"],
+    "crop_types": [
+      "rose"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -473,7 +481,7 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111968",
-    "citation": "Shacha, Arnob Das; Durjoy, Sabbir Hossain; Shikder, Md Emon; Kamal, MD Mostafa; Shoib, Md Mehedi Hasan; Bijoy, Md Hasan Imam (2025), “RoseLeafInsight: A High-Resolution Image Dataset for Rose Leaf Disease Recognition”, Mendeley Data, V1, doi: 10.17632/8chrjdxn79.1",
+    "citation": "Shacha, Arnob Das; Durjoy, Sabbir Hossain; Shikder, Md Emon; Kamal, MD Mostafa; Shoib, Md Mehedi Hasan; Bijoy, Md Hasan Imam (2025), \u201cRoseLeafInsight: A High-Resolution Image Dataset for Rose Leaf Disease Recognition\u201d, Mendeley Data, V1, doi: 10.17632/8chrjdxn79.1",
     "zip_size_bytes": 5150705758,
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/RoseLeafInsight_disease_classification",
@@ -483,10 +491,15 @@
     "name": "taro_blight_stage_classification",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": ["Nigeria", "Ghana"],
+    "location": [
+      "Nigeria",
+      "Ghana"
+    ],
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["taro"],
+    "crop_types": [
+      "taro"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -499,7 +512,7 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111869",
-    "citation": "Yinka-Banjo, Chika; Nwaneto, Chidiebere; Ugot, Ogban-Asuquo; Umeugochukwu, Obiageli; Annor, Thompson (2024), “An Image Dataset of Taro Leaf Blight Disease Collected from the West African Sub-Region”, Mendeley Data, V2, doi: 10.17632/3knm93dkc5.2",
+    "citation": "Yinka-Banjo, Chika; Nwaneto, Chidiebere; Ugot, Ogban-Asuquo; Umeugochukwu, Obiageli; Annor, Thompson (2024), \u201cAn Image Dataset of Taro Leaf Blight Disease Collected from the West African Sub-Region\u201d, Mendeley Data, V2, doi: 10.17632/3knm93dkc5.2",
     "zip_size_bytes": 1275148970,
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/taro_blight_stage_classification",
@@ -512,7 +525,9 @@
     "location": "Bangladesh",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["jute"],
+    "crop_types": [
+      "jute"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -539,7 +554,9 @@
     "location": "India",
     "environment": "Lab",
     "real_or_synthetic": "real",
-    "crop_types": ["grape"],
+    "crop_types": [
+      "grape"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -552,7 +569,7 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111716",
-    "citation": "Dharrao, Madhuri; Dharrao, Deepak; Sonawane, Rakesh; zade, Nilima (2025), “Niphad Grape Leaf Disease Dataset (NGLD)”, Mendeley Data, V5, doi: 10.17632/8nnd2ypcv3.5",
+    "citation": "Dharrao, Madhuri; Dharrao, Deepak; Sonawane, Rakesh; zade, Nilima (2025), \u201cNiphad Grape Leaf Disease Dataset (NGLD)\u201d, Mendeley Data, V5, doi: 10.17632/8nnd2ypcv3.5",
     "zip_size_bytes": 26151761,
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/grape_leaf_disease_classification",
@@ -565,7 +582,9 @@
     "location": "Vietnam",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["durian"],
+    "crop_types": [
+      "durian"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -580,7 +599,7 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111845",
-    "citation": "Thanh Truong, Nguyen; Xuan Linh, Nguyen; Thang, Cap Pham Dinh; Tuong, Le (2025), “A Durian Leaf Image Dataset of Common Diseases in Vietnam for Agricultural Diagnosis ”, Mendeley Data, V4, doi: 10.17632/pxzvksbwnj.4",
+    "citation": "Thanh Truong, Nguyen; Xuan Linh, Nguyen; Thang, Cap Pham Dinh; Tuong, Le (2025), \u201cA Durian Leaf Image Dataset of Common Diseases in Vietnam for Agricultural Diagnosis \u201d, Mendeley Data, V4, doi: 10.17632/pxzvksbwnj.4",
     "zip_size_bytes": 183109270,
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/durian_disease_classification_vietnam",
@@ -593,7 +612,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["tea"],
+    "crop_types": [
+      "tea"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -609,7 +630,7 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111769",
-    "citation": "Alam, B M Shahria; Ahammed, Fahad; Kibria, Golam; Noor, Mohammad Tahmid; Shikdar, Omar Faruq; Mahazabin, Kazi Isat; Ali, Md Nawab Yousuf (2025), “teaLeafBD”, Mendeley Data, V4, doi: 10.17632/744vznw5k2.4",
+    "citation": "Alam, B M Shahria; Ahammed, Fahad; Kibria, Golam; Noor, Mohammad Tahmid; Shikdar, Omar Faruq; Mahazabin, Kazi Isat; Ali, Md Nawab Yousuf (2025), \u201cteaLeafBD\u201d, Mendeley Data, V4, doi: 10.17632/744vznw5k2.4",
     "zip_size_bytes": 1353513825,
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/teaLeafBD_disease_classification_classification",
@@ -622,7 +643,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["mango"],
+    "crop_types": [
+      "mango"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "boundingBox",
@@ -635,7 +658,7 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111780",
-    "citation": "Kabir, Sayem ; Rashid, Mohammad Rifat Ahmmad (2024), “Image Dataset for Mango Growth Stages  Analysis”, Mendeley Data, V1, doi: 10.17632/5snwpzdtzs.1",
+    "citation": "Kabir, Sayem ; Rashid, Mohammad Rifat Ahmmad (2024), \u201cImage Dataset for Mango Growth Stages  Analysis\u201d, Mendeley Data, V1, doi: 10.17632/5snwpzdtzs.1",
     "zip_size_bytes": 76123361,
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/mango_growth_object_detection",
@@ -648,7 +671,9 @@
     "location": "Bangladesh",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["Betel Leaf"],
+    "crop_types": [
+      "Betel Leaf"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -661,7 +686,7 @@
     ],
     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111564",
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@@ -675,7 +700,9 @@
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     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["coffee"],
+    "crop_types": [
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+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -693,7 +720,7 @@
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     "documentation": "https://doi.org/10.1016/j.dib.2025.111434",
-    "citation": "Nair, Bipin (2024), “CBD_Coffee Bean Dataset”, Mendeley Data, V1, doi: 10.17632/52877z55vr.1",
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@@ -706,7 +733,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["eggplant"],
+    "crop_types": [
+      "eggplant"
+    ],
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     "input_data_format": "image_folder",
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@@ -722,7 +751,7 @@
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+    "citation": "Howlader, Shakib; Ahamed, Md. Sabbir; Mojumdar, Mayen Uddin  (2025), \u201cEggplant Leaf Disease Detection Dataset\u201d, Mendeley Data, V2, doi: 10.17632/d3ypkphghb.2",
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@@ -736,7 +765,9 @@
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     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["Olive Tree"],
+    "crop_types": [
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+    ],
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     "input_data_format": "image_folder",
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@@ -744,7 +775,7 @@
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@@ -757,7 +788,9 @@
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     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["tea"],
+    "crop_types": [
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@@ -772,7 +805,7 @@
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@@ -785,7 +818,9 @@
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     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["Turmeric"],
+    "crop_types": [
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+    ],
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@@ -799,7 +834,7 @@
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@@ -813,7 +848,9 @@
     "location": "Portugal",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["banana"],
+    "crop_types": [
+      "banana"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -824,7 +861,7 @@
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-    "citation": "Hayat, Ahatsham; Baglat, Preety; Mendonça, Fabio; Mostafa, Sheikh Shanawaz; Morgado-Dias, Fernando  (2024), “Banana Bunch Harvesting Dataset”, Mendeley Data, V1, doi: 10.17632/kjrsb7ztr9.1",
+    "citation": "Hayat, Ahatsham; Baglat, Preety; Mendon\u00e7a, Fabio; Mostafa, Sheikh Shanawaz; Morgado-Dias, Fernando  (2024), \u201cBanana Bunch Harvesting Dataset\u201d, Mendeley Data, V1, doi: 10.17632/kjrsb7ztr9.1",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/banana_bunch_maturity_classification_classification",
@@ -837,7 +874,9 @@
     "location": "Bangladesh",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["eggplant"],
+    "crop_types": [
+      "eggplant"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -851,7 +890,7 @@
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     "documentation": "https://doi.org/10.1016/j.dib.2026.112490",
-    "citation": "Hasan, Md. Zahid ; Bitto, Abu Kowshir; Bijoy, Md Hasan Imam (2025), “BrinjalFruitX: A Field-Collected Image Dataset for Machine Learning and Deep Learning-Based Disease Identification in Brinjal Fruits”, Mendeley Data, V1, doi: 10.17632/ngc58fsxgd.1",
+    "citation": "Hasan, Md. Zahid ; Bitto, Abu Kowshir; Bijoy, Md Hasan Imam (2025), \u201cBrinjalFruitX: A Field-Collected Image Dataset for Machine Learning and Deep Learning-Based Disease Identification in Brinjal Fruits\u201d, Mendeley Data, V1, doi: 10.17632/ngc58fsxgd.1",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/BrinjalFruitX_disease_classification",
@@ -914,7 +953,7 @@
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     "license": "cc-by-nc-3.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111660",
-    "citation": "T, gopu; k, uma (2025), “SIMPD V1: South Indian Medicinal Plants dataset (Version 1)”, Mendeley Data, V2, doi: 10.17632/9d89vjcghv.2",
+    "citation": "T, gopu; k, uma (2025), \u201cSIMPD V1: South Indian Medicinal Plants dataset (Version 1)\u201d, Mendeley Data, V2, doi: 10.17632/9d89vjcghv.2",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/SIMPDV1_plant_classification",
@@ -927,7 +966,11 @@
     "location": "Burkina Faso",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["tomato", "onion", "maize"],
+    "crop_types": [
+      "tomato",
+      "onion",
+      "maize"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -962,7 +1005,7 @@
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     "documentation": "https://doi.org/10.1016/j.dib.2025.111357",
-    "citation": "Appiah, Obed; Hackman, Kwame Oppong ; Diallo, Belko Abdoul Aziz; Ogunjobi, Kehinde O; Ouedraogo, Valentin; Bebe, Momo; SON, Diakalia (2024), “TOM2024”, Mendeley Data, V1, doi: 10.17632/3d4yg89rtr.1",
+    "citation": "Appiah, Obed; Hackman, Kwame Oppong ; Diallo, Belko Abdoul Aziz; Ogunjobi, Kehinde O; Ouedraogo, Valentin; Bebe, Momo; SON, Diakalia (2024), \u201cTOM2024\u201d, Mendeley Data, V1, doi: 10.17632/3d4yg89rtr.1",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/TOM2024_disease_classification",
@@ -975,7 +1018,9 @@
     "location": "Spain",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["grapes"],
+    "crop_types": [
+      "grapes"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "segmentationMask",
@@ -987,7 +1032,7 @@
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     "documentation": "https://doi.org/10.1016/j.dib.2025.111335",
-    "citation": "Pacioni, Elia; Abengozar-García, Eugenio; Macías Macías, Miguel; García Orellana, Carlos J.; González Velasco, Horacio M.; García Manso, Antonio (2024), “Vineyard dataset for pruning”, Mendeley Data, V2, doi: 10.17632/n8cs4ns97p.2",
+    "citation": "Pacioni, Elia; Abengozar-Garc\u00eda, Eugenio; Mac\u00edas Mac\u00edas, Miguel; Garc\u00eda Orellana, Carlos J.; Gonz\u00e1lez Velasco, Horacio M.; Garc\u00eda Manso, Antonio (2024), \u201cVineyard dataset for pruning\u201d, Mendeley Data, V2, doi: 10.17632/n8cs4ns97p.2",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/vineyard_pruning_segmentation",
@@ -1001,7 +1046,7 @@
     "environment": "lab",
     "real_or_synthetic": "real",
     "crop_types": [
-      "Tomato", 
+      "Tomato",
       "Papaya",
       "Mango",
       "Burmese Grape",
@@ -1019,7 +1064,7 @@
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-    "citation": "Mojumdar, Mayen Uddin ; Mamun, Md Al ; Islam, Shahrin; Hasan, Rifat (2024), “A Dataset of Common Asian Fruits for Quality Grading and Biodiversity Research”, Mendeley Data, V1, doi: 10.17632/bz65dz2pbj.1",
+    "citation": "Mojumdar, Mayen Uddin ; Mamun, Md Al ; Islam, Shahrin; Hasan, Rifat (2024), \u201cA Dataset of Common Asian Fruits for Quality Grading and Biodiversity Research\u201d, Mendeley Data, V1, doi: 10.17632/bz65dz2pbj.1",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/AFruitDB_fruit_grade_classification",
@@ -1032,7 +1077,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["turmeric"],
+    "crop_types": [
+      "turmeric"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1048,7 +1095,7 @@
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     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111435",
-    "citation": "SIAM, A K M FAZLUL KOBIR ; Nirob, Md Asraful Sharker; Bishshash, Prayma (2025), “Turmeric Plant Disease Dataset: Advancing AI for Agricultural Sustainability”, Mendeley Data, V2, doi: 10.17632/g46dvrcvwn.2",
+    "citation": "SIAM, A K M FAZLUL KOBIR ; Nirob, Md Asraful Sharker; Bishshash, Prayma (2025), \u201cTurmeric Plant Disease Dataset: Advancing AI for Agricultural Sustainability\u201d, Mendeley Data, V2, doi: 10.17632/g46dvrcvwn.2",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/turmeric_disease_classification",
@@ -1061,7 +1108,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["Black Gram"],
+    "crop_types": [
+      "Black Gram"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1075,7 +1124,7 @@
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     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2025.111347",
-    "citation": "Shoib, Md Mehedi Hasan; Saeem, Shahnewaz; Tonima, Afia Benta Aziz; Mojumdar, Mayen Uddin  (2024), “Image Dataset for Disease Detection in Black Gram (Vigna mungo) Leaves: A Resource for Machine Learning Research”, Mendeley Data, V3, doi: 10.17632/z55yrbmn2d.3",
+    "citation": "Shoib, Md Mehedi Hasan; Saeem, Shahnewaz; Tonima, Afia Benta Aziz; Mojumdar, Mayen Uddin  (2024), \u201cImage Dataset for Disease Detection in Black Gram (Vigna mungo) Leaves: A Resource for Machine Learning Research\u201d, Mendeley Data, V3, doi: 10.17632/z55yrbmn2d.3",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/black_gram_disease_classification",
@@ -1088,7 +1137,9 @@
     "location": "Cameroon",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["plum"],
+    "crop_types": [
+      "plum"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1116,7 +1167,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["plum"],
+    "crop_types": [
+      "plum"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1129,7 +1182,7 @@
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     "documentation": "https://doi.org/10.1016/j.dib.2025.111311",
-    "citation": "Durjoy, Sabbir Hossain; EMON, MD EMON SHIKDER; Mojumdar, Mayen Uddin  (2024), “Hog Plum Leaf Disease Detection Dataset”, Mendeley Data, V1, doi: 10.17632/yvtn2gp8zg.1",
+    "citation": "Durjoy, Sabbir Hossain; EMON, MD EMON SHIKDER; Mojumdar, Mayen Uddin  (2024), \u201cHog Plum Leaf Disease Detection Dataset\u201d, Mendeley Data, V1, doi: 10.17632/yvtn2gp8zg.1",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/hog_plum_leaf_disease_classification",
@@ -1142,7 +1195,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["Dragonfruit"],
+    "crop_types": [
+      "Dragonfruit"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1157,7 +1212,7 @@
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     "documentation": "https://doi.org/10.1016/j.dib.2025.111411",
-    "citation": "Sarkar, Pronob; Pranta, Gourab Kumar ; Mojumdar, Mayen Uddin  (2024), “Dragon fruit & leaf Dataset from Bangladesh for Classification and Ecological Research”, Mendeley Data, V1, doi: 10.17632/cfchfdpfw5.1",
+    "citation": "Sarkar, Pronob; Pranta, Gourab Kumar ; Mojumdar, Mayen Uddin  (2024), \u201cDragon fruit & leaf Dataset from Bangladesh for Classification and Ecological Research\u201d, Mendeley Data, V1, doi: 10.17632/cfchfdpfw5.1",
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     "hf_link": "https://huggingface.co/datasets/Project-AgML/dragonfruit_disease_classification",
@@ -1170,7 +1225,9 @@
     "location": "Bangladesh",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["tea"],
+    "crop_types": [
+      "tea"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1183,7 +1240,7 @@
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     "documentation": "https://doi.org/10.1016/j.dib.2025.111379",
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+    "citation": "Ahmad, MD Hasan (2024), \u201cAdvanced Tea Crop Disease Study: High-Resolution Dataset for Precision Agriculture and Pathological Insight\u201d, Mendeley Data, V4, doi: 10.17632/tt2smzrzrs.4",
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@@ -1230,7 +1287,7 @@
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-    "citation": "Ahmed, Md Jobayer; Saha, Ratu; Dutta , Arpon Kishore ; Mojumdar, Mayen Uddin  (2025), “Vegetable Image Dataset for Classification Models: A Bangladeshi Perspective”, Mendeley Data, V4, doi: 10.17632/b9rvg4f2st.4",
+    "citation": "Ahmed, Md Jobayer; Saha, Ratu; Dutta , Arpon Kishore ; Mojumdar, Mayen Uddin  (2025), \u201cVegetable Image Dataset for Classification Models: A Bangladeshi Perspective\u201d, Mendeley Data, V4, doi: 10.17632/b9rvg4f2st.4",
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@@ -1243,7 +1300,9 @@
     "location": "India",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["centella asiatica"],
+    "crop_types": [
+      "centella asiatica"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1255,7 +1314,7 @@
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     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2024.111150",
-    "citation": "Suryawanshi, Yogesh; Wakode, Krishna; PATIL, Kailas; chumchu, prawit (2024), “Centella Asiatica Leaf Image Dataset”, Mendeley Data, V2, doi: 10.17632/hrx2kgphy5.2",
+    "citation": "Suryawanshi, Yogesh; Wakode, Krishna; PATIL, Kailas; chumchu, prawit (2024), \u201cCentella Asiatica Leaf Image Dataset\u201d, Mendeley Data, V2, doi: 10.17632/hrx2kgphy5.2",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/centella_asiatica_leaves",
@@ -1268,7 +1327,9 @@
     "location": "Saudi Arabia",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["date palm"],
+    "crop_types": [
+      "date palm"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1286,7 +1347,7 @@
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     "license": "cc-by-4.0",
     "documentation": "https://doi.org/10.1016/j.dib.2024.110933",
-    "citation": "Namoun, Abdallah; Alkhodre, Ahmad B.; Abi Sen, Adnan Ahmad ; Alsaawy, Yazed; Almoamari, Hani (2024), “Diseases of date palm leaves dataset”, Mendeley Data, V2, doi: 10.17632/g684ghfxvg.2",
+    "citation": "Namoun, Abdallah; Alkhodre, Ahmad B.; Abi Sen, Adnan Ahmad ; Alsaawy, Yazed; Almoamari, Hani (2024), \u201cDiseases of date palm leaves dataset\u201d, Mendeley Data, V2, doi: 10.17632/g684ghfxvg.2",
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@@ -1299,7 +1360,9 @@
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     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["rice"],
+    "crop_types": [
+      "rice"
+    ],
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     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1324,7 +1387,9 @@
     "location": "India",
     "environment": "Lab",
     "real_or_synthetic": "real",
-    "crop_types": ["bay leaf"],
+    "crop_types": [
+      "bay leaf"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1336,7 +1401,7 @@
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     "documentation": "https://doi.org/10.1016/j.dib.2024.111024",
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+    "citation": "PAYGUDE, PRIYANKA; CHAVAN, PRASHANT; Gayakwad, Milind (2024), \u201cIndian Bay Leaves or Cinnamomum Tamala Leaves Dataset\u201d, Mendeley Data, V2, doi: 10.17632/s9t7sr52wg.2",
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@@ -1349,7 +1414,10 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["Bean", "cowpea"],
+    "crop_types": [
+      "Bean",
+      "cowpea"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1364,7 +1432,7 @@
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+    "citation": "Rashid, Mohammad Rifat Ahmmad; Hasan, Mahamudul; Gani, Raiyan ; Kamara, Raka ; Tarin, Taslima Khan ; Rabbi, Sheikh Fajlay  (2024), \u201cImage Dataset for Bean and Cowpea Plant Leaf Disease Detection and Freshness Assessment from Bangladesh \u201d, Mendeley Data, V1, doi: 10.17632/ykvcrjffzd.1",
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@@ -1377,7 +1445,9 @@
     "location": "Bangladesh",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["Papaya"],
+    "crop_types": [
+      "Papaya"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
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@@ -1391,7 +1461,7 @@
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+    "citation": "Sarker, Arpita ; Mustofa, Sumaya; Ahad, Md Taimur  (2024), \u201cBDPapayaLeaf: A annotation based image dataset of papaya leaf disease.\u201d, Mendeley Data, V2, doi: 10.17632/p997fvf526.2",
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@@ -1446,7 +1516,7 @@
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+    "citation": "Islam, Mohammad Manzurul; Rahman, Sanjida; Hoque, Nahida; Mamun, Md. Al; Moheuddin, Md. Sultan (2024), \u201cREMP: A Unique Dataset of Rare and Endangered Medicinal Plants in Bangladesh\u201d, Mendeley Data, V1, doi: 10.17632/hnwrxg8zm8.1",
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@@ -1459,7 +1529,9 @@
     "location": "Bangladesh",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["cotton"],
+    "crop_types": [
+      "cotton"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1477,7 +1549,7 @@
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-    "citation": "Bishshash, Prayma; Nirob, Md Asraful Sharker; Shikder, Md. Habibur; Sarower, Afjal (2024), “SAR-CLD-2024: A Comprehensive Dataset for Cotton Leaf Disease Detection”, Mendeley Data, V2, doi: 10.17632/b3jy2p6k8w.2",
+    "citation": "Bishshash, Prayma; Nirob, Md Asraful Sharker; Shikder, Md. Habibur; Sarower, Afjal (2024), \u201cSAR-CLD-2024: A Comprehensive Dataset for Cotton Leaf Disease Detection\u201d, Mendeley Data, V2, doi: 10.17632/b3jy2p6k8w.2",
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@@ -1490,7 +1562,10 @@
     "location": "India",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["banana", "guava"],
+    "crop_types": [
+      "banana",
+      "guava"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
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@@ -1502,7 +1577,7 @@
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+    "citation": "KUMARI, ABIBAN; Singh, Jaswinder (2024), \u201cFruits (Banana and Guava) datasets for non-destructive quality classifications\u201d, Mendeley Data, V2, doi: 10.17632/56td5w4wz2.2",
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@@ -1515,7 +1590,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["mango"],
+    "crop_types": [
+      "mango"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1532,7 +1609,7 @@
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+    "citation": "Islam, Mohammad Manzurul; Das, Aritra; Hasan, Md. Rakibul; Rashid, Mohammad Rifat Ahmmad (2024), \u201cImage Dataset of Bangladeshi Mango Leaf\u201d, Mendeley Data, V5, doi: 10.17632/nnh69sng8p.5",
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@@ -1545,7 +1622,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["Epipremnum aureum"],
+    "crop_types": [
+      "Epipremnum aureum"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
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@@ -1557,7 +1636,7 @@
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-    "citation": "Ahmad, MD Hasan (2024), “Advanced Dataset on Money Plant Diseases for AI Pathology Research”, Mendeley Data, V3, doi: 10.17632/rzjww3vdxt.3",
+    "citation": "Ahmad, MD Hasan (2024), \u201cAdvanced Dataset on Money Plant Diseases for AI Pathology Research\u201d, Mendeley Data, V3, doi: 10.17632/rzjww3vdxt.3",
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@@ -1570,7 +1649,9 @@
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     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["Radish"],
+    "crop_types": [
+      "Radish"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1584,7 +1665,7 @@
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+    "citation": "Rashid, Mohammad Rifat Ahmmad; Hasan, Mahamudul; Gani, Raiyan ; Tarin, Taslima Khan ; Kamara, Raka ; Rabbi, Sheikh Fajlay  (2024), \u201cImage Dataset for Radish Plant Leaf Disease Detection and Freshness Assessment from Bangladesh \u201d, Mendeley Data, V1, doi: 10.17632/s973cz2jcd.1",
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@@ -1597,7 +1678,9 @@
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     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["spinach"],
+    "crop_types": [
+      "spinach"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1611,7 +1694,7 @@
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-    "citation": "Sayeem , Adnan Rahman; Omi  , Jannatul Ferdous; Hasan , Mehedi; Mojumdar, Mayen Uddin  (2024), “Malabar Spinach Disease Detection Dataset”, Mendeley Data, V2, doi: 10.17632/sy69db2nz5.2",
+    "citation": "Sayeem , Adnan Rahman; Omi  , Jannatul Ferdous; Hasan , Mehedi; Mojumdar, Mayen Uddin  (2024), \u201cMalabar Spinach Disease Detection Dataset\u201d, Mendeley Data, V2, doi: 10.17632/sy69db2nz5.2",
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@@ -1624,7 +1707,9 @@
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     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["banana"],
+    "crop_types": [
+      "banana"
+    ],
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     "input_data_format": "image_folder",
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@@ -1639,7 +1724,7 @@
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+    "citation": "Ferdaus, Md Hasanul; Prito, Rizvee Hassan; Rasel, Ahmed Abdal Shafi Rasel; Ahmed, Masud; Saykot, Md. Jahid Hassan; Shanta, Shanjida Sultan; Akter, Sonali; Das, Ankan Chandra; Islam, Mohammad Manzurul; Hasan, Mahamudul; Ali, Sawkat (2024), \u201cBananaImageBD: A Comprehensive Image Dataset of Common Banana Varieties with Different Ripeness Stages in Bangladesh.\u201d, Mendeley Data, V2, doi: 10.17632/ptfscwtnyz.2",
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@@ -1652,7 +1737,9 @@
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     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["banana"],
+    "crop_types": [
+      "banana"
+    ],
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@@ -1667,7 +1754,7 @@
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+    "citation": "Ferdaus, Md Hasanul; Prito, Rizvee Hassan; Rasel, Ahmed Abdal Shafi Rasel; Ahmed, Masud; Saykot, Md. Jahid Hassan; Shanta, Shanjida Sultan; Akter, Sonali; Das, Ankan Chandra; Islam, Mohammad Manzurul; Hasan, Mahamudul; Ali, Sawkat (2024), \u201cBananaImageBD: A Comprehensive Image Dataset of Common Banana Varieties with Different Ripeness Stages in Bangladesh.\u201d, Mendeley Data, V2, doi: 10.17632/ptfscwtnyz.2",
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@@ -1680,7 +1767,9 @@
     "location": "Bangladesh",
     "environment": "lab",
     "real_or_synthetic": "real",
-    "crop_types": ["lemon"],
+    "crop_types": [
+      "lemon"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1700,7 +1789,7 @@
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-    "citation": "Siam, A K M Fazlul Kobir; Bishshash, Prayma; Nirob, Md Asraful Sharker; Mamun, Sajib Bin; Assaduzzaman, Md; Noori, Sheak Rashed Haider  (2024), “Comprehensive Lemon Leaf Disease Dataset for Advanced Detection and Sustainable Agriculture”, Mendeley Data, V1, doi: 10.17632/44nrn4593f.1",
+    "citation": "Siam, A K M Fazlul Kobir; Bishshash, Prayma; Nirob, Md Asraful Sharker; Mamun, Sajib Bin; Assaduzzaman, Md; Noori, Sheak Rashed Haider  (2024), \u201cComprehensive Lemon Leaf Disease Dataset for Advanced Detection and Sustainable Agriculture\u201d, Mendeley Data, V1, doi: 10.17632/44nrn4593f.1",
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@@ -1713,7 +1802,9 @@
     "location": "Bangladesh",
     "environment": "field",
     "real_or_synthetic": "real",
-    "crop_types": ["lentil"],
+    "crop_types": [
+      "lentil"
+    ],
     "sensor_modality": "rgb",
     "input_data_format": "image_folder",
     "annotation_format": "classLabel",
@@ -1726,13 +1817,255 @@
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     "documentation": "https://doi.org/10.1016/j.dib.2024.111224",
-    "citation": "Mahamud, Eram ; Tapos, Md Assaduzzaman (2024), “Lentil Plant Disease Image Dataset (4 Class)”, Mendeley Data, V2, doi: 10.17632/7vb77bz2st.2",
+    "citation": "Mahamud, Eram ; Tapos, Md Assaduzzaman (2024), \u201cLentil Plant Disease Image Dataset (4 Class)\u201d, Mendeley Data, V2, doi: 10.17632/7vb77bz2st.2",
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     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/lentil_disease_classification",
     "examples_image_url": "/img/agml/sample_images/lentil_disease_classification_sample.png"
+  },
+  {
+    "name": "GYMNSA_pear_rust_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "disease_detection",
+    "location": "Germany",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "pear"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 746,
+    "classes": [],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2025.111271",
+    "citation": "Ma\u00df, Virginia; Alirezazadeh, Pendar; Seidl-Schulz, Johannes; Leipnitz, Matthias; Fritzsche, Eric; Ibraheem, Rasheed Ali Adam; Geyer, Martin; Pflanz, Michael; Reim, Stefanie (2024), \u201cGYMNSA dataset\u201d, Mendeley Data, V1, doi: 10.17632/44kjgc4gkc.1",
+    "zip_size_bytes": 207266525,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/GYMNSA_pear_rust_detection",
+    "examples_image_url": "/img/agml/sample_images/GYMNSA_pear_rust_detection_sample.png"
+  },
+  {
+    "name": "oyster_mushroom_maturity_detection",
+    "machine_learning_task": "object_detection",
+    "agricultural_task": "maturity_classification",
+    "location": "Turkey",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "Oyster Mushroom"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "boundingBox",
+    "num_images": 555,
+    "classes": [
+      "Immature",
+      "Mature"
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+    "license": "cc-by-4.0",
+    "documentation": "",
+    "citation": "Duman, Sonay; Elewi, Abdullah; Hajhamed, Abdulsalam; Khankan, Rasheed ; Souag, Amina; Ahmed, Asma (2024), \u201cAnnotated Oyster Mushroom Images\u201d, Mendeley Data, V1, doi: 10.17632/hf55tkx489.1",
+    "zip_size_bytes": 281852598,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/oyster_mushroom_maturity_detection",
+    "examples_image_url": "/img/agml/sample_images/oyster_mushroom_maturity_detection_sample.png"
+  },
+  {
+    "name": "guava_disease_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "Bangladesh",
+    "environment": "lab",
+    "real_or_synthetic": "real",
+    "crop_types": [
+      "Guava"
+    ],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 3049,
+    "augmented_num_images": 20344,
+    "augmented_zip_size_bytes": 7616932478,
+    "classes": [
+      "Anthracnose",
+      "Canker",
+      "Dot",
+      "Healthy",
+      "Rust",
+      "Scab",
+      "Styler end root"
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+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2025.111378",
+    "citation": "Shihab, Md Montsir Rahman; Saim, Nafiu Islam; Mojumdar, Mayen Uddin  (2024), \u201cImage Dataset for Detection and classification of Diseases of Guava Fruits and Leaves \u201d, Mendeley Data, V1, doi: 10.17632/fspx44mwfp.1",
+    "zip_size_bytes": 2156708924,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/guava_disease_classification",
+    "examples_image_url": "/img/agml/sample_images/guava_disease_classification_sample.png"
+  },
+  {
+    "name": "COLD_chili_leaf_disease_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "India",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": ["Chili"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
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+    "classes": [
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+      "healthy",
+      "mites_and_trips",
+      "nutritional",
+      "powdery mildew"
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+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110524",
+    "citation": "M P, Aishwarya; Reddy, Padmanabha  (2024), “chilli dataset”, Mendeley Data, V2, doi: 10.17632/tf9dtfz9m6.2",
+    "zip_size_bytes": 13504653,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/COLD_chili_leaf_disease_classification",
+    "examples_image_url": "/img/agml/sample_images/COLD_chili_leaf_disease_classification_sample.png"
+  },
+  {
+    "name": "EfficientMaize_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "quality_classification",
+    "location": "Ghana",
+    "environment": "lab",
+    "real_or_synthetic": "real",
+    "crop_types": ["maize"],
+    "sensor_modality": "rgb",
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+    "augmented_zip_size_bytes": 55380083,
+    "classes": [
+      "Bad",
+      "Good"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110261",
+    "citation": "Asante, Emmanuel ; Appiah, Obed; APPIAHENE, PETER (2023), “Lightweight Dataset for Maize Classification on Resource-Constrained Devices”, Mendeley Data, V2, doi: 10.17632/r6vvm5jkh6.2",
+    "zip_size_bytes": 7328640,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/EfficientMaize_classification",
+    "examples_image_url": "/img/agml/sample_images/EfficientMaize_classification_sample.png"
+  },
+  {
+    "name": "mulberry_leaf_variety_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "variety_classification",
+    "location": "Thailand",
+    "environment": "Field",
+    "real_or_synthetic": "real",
+    "crop_types": ["mulberry"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 5262,
+    "classes": [
+      "BlackAustralia",
+      "BlackOodTurkey",
+      "Buriram60",
+      "ChiangMai60",
+      "ChiangMaiBuriram60",
+      "Kamphaengsaeng42",
+      "RedKing",
+      "TaiwanMeacho",
+      "TaiwanStraberry",
+      "WhiteKing"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110281",
+    "citation": "Chom, thipwimon; surinta, olarik (2024), “Mulberry Leaf Dataset”, Mendeley Data, V3, doi: 10.17632/ds45yy9jrc.3",
+    "zip_size_bytes": 101911765,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/mulberry_leaf_variety_classification",
+    "examples_image_url": "/img/agml/sample_images/mulberry_leaf_variety_classification_sample.png"
+  },
+  {
+    "name": "rice_grain_variety_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "variety_classification",
+    "location": "Bangladesh",
+    "environment": "Lab",
+    "real_or_synthetic": "real",
+    "crop_types": ["rice"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 4730,
+    "augmented_num_images": 23650,
+    "augmented_zip_size_bytes": 821150919,
+    "classes": [
+      "Amon",
+      "BR28",
+      "BR29",
+      "Bashful",
+      "Bashmoti",
+      "Chinigura_Polao",
+      "Ganjiya",
+      "Gutisharna",
+      "Jirashail",
+      "Katari_Polao",
+      "Katarivog",
+      "Lal_Aush",
+      "Lal_Binni",
+      "Lal_Biroi",
+      "Najirshail",
+      "Paijam",
+      "Red_Cargo",
+      "Shampakatari",
+      "Shorna5",
+      "Subol_Lota"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110442",
+    "citation": "Islam, Md Masudul; Himel, Galib Muhammad Shahriar; Moazzam, Md. Golam; Uddin, Mohammad Shorif (2024), “Aruzz22.5K: An Image Dataset of Rice Varieties”, Mendeley Data, V4, doi: 10.17632/3mn9843tz2.4",
+    "zip_size_bytes": 281483801,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/rice_grain_variety_classification",
+    "examples_image_url": "/img/agml/sample_images/rice_grain_variety_classification_sample.png"
+  },
+  {
+    "name": "COLD_onion_leaf_disease_classification",
+    "machine_learning_task": "image_classification",
+    "agricultural_task": "disease_classification",
+    "location": "India",
+    "environment": "field",
+    "real_or_synthetic": "real",
+    "crop_types": ["Onion"],
+    "sensor_modality": "rgb",
+    "input_data_format": "image_folder",
+    "annotation_format": "classLabel",
+    "num_images": 815,
+    "augmented_num_images": 4502,
+    "augmented_zip_size_bytes": 35910198,
+    "classes": [
+      "Iris yellow virus",
+      "Stemphylium leaf blight and collectrichum leaf blight",
+      "healthy",
+      "purple blotch"
+    ],
+    "license": "cc-by-4.0",
+    "documentation": "https://doi.org/10.1016/j.dib.2024.110524",
+    "citation": "M P, Aishwarya; Reddy, Padmanabha  (2024), “Onion dataset”, Mendeley Data, V2, doi: 10.17632/7nxxn4gj5s.2",
+    "zip_size_bytes": 16399214,
+    "source": "huggingface",
+    "hf_link": "https://huggingface.co/datasets/Project-AgML/COLD_onion_leaf_disease_classification",
+    "examples_image_url": "/img/agml/sample_images/COLD_onion_leaf_disease_classification_sample.png"
   }
 
 
 
+
 ]
\ No newline at end of file
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