diff --git a/static/data/dataset_history.json b/static/data/dataset_history.json index 039bebb..37d5e76 100644 --- a/static/data/dataset_history.json +++ b/static/data/dataset_history.json @@ -1,6 +1,6 @@ { "_readme": "Hand-maintained going forward — do not regenerate. To add a snapshot, run `python3 scripts/append_dataset_snapshot.py` (counts the live static/data/{datasets,hf_datasets}.json the same way src/lib/datasets.ts computeDatasetStats does: top-level datasets only, iNatAg-mini's images excluded) or append to `points` by hand: { period: \"\", date: \"\", datasetCount: , imageCount: }.", - "generatedAt": "2026-09-17", + "generatedAt": "2026-09-18", "annotations": [ { "atPeriod": "May 30", @@ -171,6 +171,12 @@ "date": "2026-09-17", "datasetCount": 323, "imageCount": 6791947 + }, + { + "period": "Sep 19", + "date": "2026-09-18", + "datasetCount": 346, + "imageCount": 6903124 } ] } diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index 8d21a76..e90113a 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -13510,5 +13510,898 @@ "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/USU-Corn-WeedDB_detection", "examples_image_url": "/img/agml/sample_images/USU-Corn-WeedDB_detection_sample.webp" + }, + { + "name": "Sesame_Aerial_weed_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "weed_segmentation", + "location": [ + "Mardan, Khyber Pakhtunkhwa, Pakistan", + "Ballo Shahabal Village, Jhang, Punjab, Pakistan" + ], + "lat_lon": [], + "country": "Pakistan", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "sesame" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Mavic mini drone (1/2.3\" CMOS sensor, 1920x1080 resolution)", + "Agrocam NDVI sensor" + ], + "collection_period": "", + "platform": "drone", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 160, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1007/s11119-023-10027-7", + "citation": "Moazzam, Imran (2023), “SeSame / Weed Aerial Dataset”, Mendeley Data, V2, doi: 10.17632/9pgv3ktk33.2", + "bibtex": "@article{moazzam2023w,\n title={A W-shaped convolutional network for robust crop and weed classification in agriculture},\n author={Moazzam, Syed Imran and Nawaz, Tahir and Qureshi, Waqar S. and Khan, Umar S. and Tiwana, Mohsin Islam},\n journal={Precision Agriculture},\n volume={24},\n pages={2002-2018},\n year={2023},\n publisher={Springer US}\n}", + "zip_size_bytes": 1453744604, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/Sesame_Aerial_weed_segmentation", + "examples_image_url": "/img/agml/sample_images/Sesame_Aerial_weed_segmentation_sample.webp" + }, + { + "name": "WeedsGalore_maize_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "weed_segmentation", + "location": [ + "Marquardt, Potsdam, Germany" + ], + "lat_lon": [ + "52 27 50.6, 12 57 27.5" + ], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "DJI Phantom P4 Multispectral" + ], + "collection_period": "May 25, May 30, June 6, June 15", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 780, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1109/WACV61041.2025.00467", + "citation": "https://github.com/GFZ/weedsgalore", + "bibtex": "@inproceedings{celikkan2025weedsgalore, \n title={WeedsGalore: A multispectral and multitemporal UAV-based dataset for crop and weed segmentation in agricultural maize fields}, \n author={Celikkan, Ekin and Kunzmann, Timo and Yeskaliyev, Yertay and Itzerott, Sibylle and Klein, Nadja and Herold, Martin}, \n booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, \n pages={4767--4777}, \n year={2025}, \n organization={IEEE}}", + "zip_size_bytes": 337727881, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/WeedsGalore_maize_segmentation", + "examples_image_url": "/img/agml/sample_images/WeedsGalore_maize_segmentation_sample.webp" + }, + { + "name": "WPDv2_BBCH_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "weed_classification", + "location": [ + "Saint-Jean-sur-Richelieu, Québec, Canada" + ], + "lat_lon": [], + "country": "Canada", + "environment": "greenhouse", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DELL UltraSharp 4 K" + ], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 3920, + "classes": [ + "Amaranthus_retroflexus (AMARE)", + "Amaranthus_tuberculatus (AMATU)", + "Chenopodium_album (CHEAL)", + "Echinochloa_crus-galli (ECHCG)", + "Setaria_faberi (SETFA)" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1038/s41598-025-33961-0", + "citation": "https://github.com/etiennelord/TaxonomicalLoss", + "bibtex": "@article{fontaine2026taxonomical, \n title={Taxonomical loss for weed seedlings image classification}, \n author={Fontaine, Hans-Olivier and Foucher, Samuel and Fallon, Edith and Simard, Marie-Josee and Lord, Etienne}, \n journal={Scientific Reports}, \n volume={16}, \n number={1}, \n pages={3837}, \n year={2026},\n publisher={Nature Publishing Group UK London}}", + "zip_size_bytes": 379371761, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/WPDv2_BBCH_classification", + "examples_image_url": "/img/agml/sample_images/WPDv2_BBCH_classification_sample.webp" + }, + { + "name": "DeepWeeds_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "weed_classification", + "location": [], + "lat_lon": [], + "country": "Australia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "FLIR Blackfly 23S6C" + ], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 17509, + "classes": [ + "0", + "1", + "2", + "3", + "4", + "5", + "6", + "7", + "8" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1038/s41598-018-38343-3", + "citation": "https://github.com/AlexOlsen/DeepWeeds", + "bibtex": "@article{olsen2019deepweeds,\n title={DeepWeeds: A multiclass weed species image dataset for deep learning},\n author={Olsen, Alex and Konovalov, Dmitry A and Philippa, Bronson and Ridd, Peter and Wood, Jake C and Johns, Jamie and Banks, Wesley and Girgenti, Benjamin and Kenny, Owen and Whinney, James and others},\n journal={Scientific reports},\n volume={9},\n number={1},\n pages={2058},\n year={2019},\n publisher={Nature Publishing Group UK London}}", + "zip_size_bytes": 493506267, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/DeepWeeds_classification", + "examples_image_url": "/img/agml/sample_images/DeepWeeds_classification_sample.webp" + }, + { + "name": "EuroSat_LULC_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "land_use_classification", + "location": [], + "lat_lon": [], + "country": "", + "environment": "", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "", + "platform": "satellite", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 27000, + "classes": [ + "AnnualCrop", + "Forest", + "HerbaceousVegetation", + "Highway", + "Industrial", + "Pasture", + "PermanentCrop", + "Residential", + "River", + "SeaLake" + ], + "license": "MIT", + "documentation": "https://doi.org/10.48550/arXiv.1709.00029", + "citation": "Helber, P., Bischke, B., Dengel, A., & Borth, D. (2018). EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification [Dataset]. In EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (Vol. 12, Issue 7, pp. 2217-2226). Zenodo. Introducing Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification, Valencia, Spain. https://doi.org/10.5281/zenodo.7711810", + "bibtex": "@article{helber2019eurosat,\n title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},\n author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},\n journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},\n volume={12},\n number={7},\n pages={2217--2226},\n year={2019},\n publisher={IEEE}}", + "zip_size_bytes": 3002781156, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/EuroSat_LULC_classification", + "examples_image_url": "/img/agml/sample_images/EuroSat_LULC_classification_sample.webp" + }, + { + "name": "WE3DS_weed_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "weed_segmentation", + "location": [ + "University of Natural Resources and Life Sciences, Vienna, Austria" + ], + "lat_lon": [ + "48 20, 16 56" + ], + "country": "Austria", + "environment": "", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "XIMEA MC023CG-SY" + ], + "collection_period": "2020, 2021", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 2568, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/s23052713", + "citation": "Kitzler, F., Barta, N., Neugschwandtner, R. W., Gronauer, A., & Motsch, V. (2023). WE3DS: An RGB-D image dataset for semantic segmentation in agriculture [Dataset]. In Sensors (Vol. 23, Issue 5, p. 2713). Zenodo. https://doi.org/10.5281/zenodo.7457983", + "bibtex": "@article{kitzler2023we3ds,\n title={WE3DS: An RGB-D image dataset for semantic segmentation in agriculture},\n author={Kitzler, Florian and Barta, Norbert and Neugschwandtner, Reinhard W and Gronauer, Andreas and Motsch, Viktoria},\n journal={Sensors},\n volume={23},\n number={5},\n pages={2713},\n year={2023},\n publisher={MDPI}}", + "zip_size_bytes": 10786781504, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/WE3DS_weed_segmentation", + "examples_image_url": "/img/agml/sample_images/WE3DS_weed_segmentation_sample.webp" + }, + { + "name": "carrot_weed_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "weed_segmentation", + "location": [ + "Lincolnshire, UK" + ], + "lat_lon": [], + "country": "UK", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "carrot" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "TeledyneDALSA Genie Nano" + ], + "collection_period": "June 2017", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 20, + "classes": [], + "license": "cc-by-nc-sa-4.0", + "documentation": "https://doi.org/10.1002/rob.21869", + "citation": "Petra Bosilj, Erchan Aptoula, Tom Duckett, and Grzegorz Cielniak: “Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture”, Journal of Field Robotics (2019)", + "bibtex": "@article{bosilj2020transfer,\n title={Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture},\n author={Bosilj, Petra and Aptoula, Erchan and Duckett, Tom and Cielniak, Grzegorz},\n journal={Journal of Field Robotics},\n volume={37},\n number={1},\n pages={7--19},\n year={2020},\n publisher={Wiley Online Library}}", + "zip_size_bytes": 205422097, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/carrot_weed_segmentation", + "examples_image_url": "/img/agml/sample_images/carrot_weed_segmentation_sample.webp" + }, + { + "name": "onion_weed_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "weed_segmentation", + "location": [ + "Lincolnshire, UK" + ], + "lat_lon": [], + "country": "UK", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "onion" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "TeledyneDALSA Genie Nano" + ], + "collection_period": "April 2017", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 20, + "classes": [], + "license": "cc-by-nc-sa-4.0", + "documentation": "https://doi.org/10.1002/rob.21869Digital Object Identifier (DOI)", + "citation": "Petra Bosilj, Erchan Aptoula, Tom Duckett, and Grzegorz Cielniak: “Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture”, Journal of Field Robotics (2019)", + "bibtex": "@article{bosilj2020transfer,\n title={Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture},\n author={Bosilj, Petra and Aptoula, Erchan and Duckett, Tom and Cielniak, Grzegorz},\n journal={Journal of Field Robotics},\n volume={37},\n number={1},\n pages={7--19},\n year={2020},\n publisher={Wiley Online Library}}", + "zip_size_bytes": 147436484, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/onion_weed_segmentation", + "examples_image_url": "/img/agml/sample_images/onion_weed_segmentation_sample.webp" + }, + { + "name": "PhenoBench_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "weed_segmentation", + "location": [ + "Campus Klein-Altendorf farm, University of Bonn, Germany" + ], + "lat_lon": [ + "50 37.51, 6 59.32" + ], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Chenopodium album", + "Polygonum aviculare", + "Thlaspi arvense", + "Persicaria lapathifolia", + "Bilderdykia convolvulus", + "Polygonum hydropiper" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI M600" + ], + "collection_period": "May 15, May 26, June 6, 2020, May 20, May 28, June 1, and June 10, 2021", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 2179, + "classes": [], + "license": "cc-by-sa-4.0", + "documentation": "https://doi.org/10.48550/arXiv.2306.04557", + "citation": "https://www.phenobench.org/dataset.html", + "bibtex": "@article{weyler2024phenobench,\n title={Phenobench: A large dataset and benchmarks for semantic image interpretation in the agricultural domain},\n author={Weyler, Jan and Magistri, Federico and Marks, Elias and Chong, Yue Linn and Sodano, Matteo and Roggiolani, Gianmarco and Chebrolu, Nived and Stachniss, Cyrill and Behley, Jens},\n journal={IEEE transactions on pattern analysis and machine intelligence},\n volume={46},\n number={12},\n pages={9583--9594},\n year={2024},\n publisher={IEEE}}", + "zip_size_bytes": 5667042196, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/PhenoBench_segmentation", + "examples_image_url": "/img/agml/sample_images/PhenoBench_segmentation_sample.webp" + }, + { + "name": "CWF788_weed_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "weedTongzhou, Beijing", + "location": [ + "Tongzhou, Beijing, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon PowerShot SX150 IS", + "iPhone 6", + "Huawei Note 8" + ], + "collection_period": "May 23, June 18, 2018", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 788, + "classes": [], + "license": "MIT", + "documentation": "https://doi.org/10.1109/ACCESS.2019.2942158", + "citation": "https://github.com/ZhangXG001/Real-Time-Crop-Recognition", + "bibtex": "@article{li2019real,\n title={Real-time crop recognition in transplanted fields with prominent weed growth: a visual-attention-based approach},\n author={Li, Nan and Zhang, Xiaoguang and Zhang, Chunlong and Guo, Huiwen and Sun, Zhe and Wu, Xinyu},\n journal={IEEE Access},\n volume={7},\n pages={185310--185321},\n year={2019},\n publisher={IEEE}}", + "zip_size_bytes": 29559326, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/CWF788_weed_segmentation", + "examples_image_url": "/img/agml/sample_images/CWF788_weed_segmentation_sample.webp" + }, + { + "name": "early-crop-weed_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "weed_classification", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 508, + "classes": [ + "black_nightsade", + "cotton", + "tomato", + "velvet_leaf " + ], + "license": "cc-by-4.0", + "documentation": "", + "citation": "https://github.com/AUAgroup/early-crop-weed", + "bibtex": "", + "zip_size_bytes": 2573393067, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/early-crop-weed_classification", + "examples_image_url": "/img/agml/sample_images/early-crop-weed_classification_sample.webp" + }, + { + "name": "CottonWeedID15_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "weed_classification", + "location": [], + "lat_lon": [], + "country": "United States of America", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cotton" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "2020, 2021", + "platform": "handhled", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 5187, + "classes": [ + "Carpetweeds", + "Crabgrass", + "Eclipta", + "Goosegrass", + "Morningglory", + "Nutsedge", + "PalmerAmaranth", + "Prickly Sida", + "Purslane", + "Ragweed", + "Sicklepod", + "SpottedSpurge", + "SpurredAnoda", + "Swinecress", + "Waterhemp" + ], + "license": "cc-by-nc-4.0", + "documentation": "https://doi.org/10.1038/s41598-026-51099-5", + "citation": "https://www.kaggle.com/datasets/yuzhenlu/cottonweedid15", + "bibtex": "@article{chen2022performance,\n title={Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems},\n author={Chen, Dong and Lu, Yuzhen and Li, Zhaojian and Young, Sierra},\n journal={Computers and Electronics in Agriculture},\n volume={198},\n pages={107091},\n year={2022},\n publisher={Elsevier}}", + "zip_size_bytes": 10352034071, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/CottonWeedID15_classification", + "examples_image_url": "/img/agml/sample_images/CottonWeedID15_classification_sample.webp" + }, + { + "name": "Dhan-Shomadhan_rice_leaf_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "Dhaka, Bangladesh" + ], + "lat_lon": [], + "country": "Bangladesh", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "rice" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Vivo Y15" + ], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 1106, + "classes": [ + "Brown Spot", + "Browon Spot", + "Leaf Scaled", + "Rice Blast", + "Rice Tungro", + "Rice Turgro", + "Shath Blight", + "Sheath Blight" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.48550/arXiv.2309.07515", + "citation": "Hossain, Md Fahad; Abujar , Sheikh ; Noori, Sheak Rashed Haider ; Hossain, Syed Akhter (2021), “Dhan-Shomadhan: A Dataset of Rice Leaf Disease Classification for Bangladeshi Local Rice”, Mendeley Data, V1, doi: 10.17632/znsxdctwtt.1", + "bibtex": "@article{hossain2023dhan,\n title={Dhan-Shomadhan: A dataset of rice leaf disease classification for Bangladeshi local rice},\n author={Hossain, Md Fahad},\n journal={arXiv preprint arXiv:2309.07515},\n year={2023}}", + "zip_size_bytes": 669798386, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/Dhan-Shomadhan_rice_leaf_disease_classification", + "examples_image_url": "/img/agml/sample_images/Dhan-Shomadhan_rice_leaf_disease_classification_sample.webp" + }, + { + "name": "Musa_banana_tier_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "crop_classification", + "location": [ + "Carmen, Philippines", + "Cebu, Philippines" + ], + "lat_lon": [], + "country": "Philippines", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "banana" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "A4Tech PK-910H" + ], + "collection_period": "", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 1164, + "classes": [ + "1", + "2", + "3", + "4" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2022.108856", + "citation": "Piedad, Eduardo Jr; Ferrer, Laura Vithalie; Pojas, Glydel; Cascabel, Honey Faith; Pantilgan, Rosemarie; Larada, Julaiza; Cabinatan, Ian Paul (2018), “Tier-based Dataset: Musa-Acuminata Banana Fruit Species”, Mendeley Data, V2, doi: 10.17632/zk3tkxndjw.2", + "bibtex": "@article{piedad2023post,\n title={Post-harvested Musa acuminata Banana Tiers Dataset},\n author={Piedad, Eduardo Jr and Caladcad, June Anne},\n journal={Data in Brief},\n volume={46},\n pages={108856},\n year={2023},\n publisher={Elsevier}\n}", + "zip_size_bytes": 62609011, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/Musa_banana_tier_classification", + "examples_image_url": "/img/agml/sample_images/Musa_banana_tier_classification_sample.webp" + }, + { + "name": "pepper_disease_pest_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_detection", + "location": [ + "Shouguang City, Shandong Province, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "pepper" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "2022-2023", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 26377, + "classes": [ + "Alternaria_Boltch", + "Brown_Spot", + "Grey_spot", + "Mosaic", + "Rust" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1186/s13007-025-01387-4", + "citation": "liu, jun (2024), “vegetable disease”, Mendeley Data, V1, doi: 10.17632/tg3z7xxkdb.1", + "bibtex": "@article{wang2025advanced,\n title={An advanced deep learning method for pepper diseases and pests detection},\n author={Wang, Xuewei and Liu, Jun and Chen, Qian},\n journal={Plant Methods},\n volume={21},\n pages={70},\n year={2025},\n publisher={BioMed Central}\n}", + "zip_size_bytes": 887159399, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/pepper_disease_pest_classification", + "examples_image_url": "/img/agml/sample_images/pepper_disease_pest_classification_sample.webp" + }, + { + "name": "OPPD_plant_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Research Centre Flakkebjerg, Aarhus University, Denmark" + ], + "lat_lon": [ + "55 19 28.4736, 11 23 24.0144" + ], + "country": "Denmark", + "environment": "greenhouse", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Flir GS3-U3-123S6C" + ], + "collection_period": "2017, 2018, 2019", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 7590, + "classes": [ + "ALOMY", + "ANGAR", + "APESV", + "ARTVU", + "AVEFA", + "BROST", + "BRSNN", + "CAPBP", + "CENCY", + "CHEAL", + "CHYSE", + "CIRAR", + "CONAR", + "EPHHE", + "EPHPE", + "EROCI", + "FUMOF", + "GALAP", + "GERMO", + "LAPCO", + "LOLMU", + "LYCAR", + "MATCH", + "MATIN", + "MELNO", + "MYOAR", + "PAPRH", + "PLALA", + "PLAMA", + "POAAN", + "POLAV", + "POLCO", + "POLLA", + "POLPE", + "RUMCR", + "SENVU", + "SINAR", + "SOLNI", + "SONOL", + "STEME", + "THLAR", + "URTUR", + "VERAR", + "VERPE", + "VICHI", + "VIOAR" + ], + "license": "cc-by-nc-sa-4.0", + "documentation": "https://doi.org/10.3390/rs12081246", + "citation": "https://vision.eng.au.dk/open-plant-phenotyping-database/", + "bibtex": "@article{leminen2020open,\n title={Open plant phenotype database of common weeds in Denmark},\n author={Leminen Madsen, Simon and Mathiassen, Solvejg Kopp and Dyrmann, Mads and Laursen, Morten Stigaard and Paz, Laura-Carlota and Jorgensen, Rasmus Nyholm},\n journal={Remote Sensing},\n volume={12},\n number={8},\n pages={1246},\n year={2020},\n publisher={MDPI}}", + "zip_size_bytes": 44694655707, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/OPPD_plant_detection", + "examples_image_url": "/img/agml/sample_images/OPPD_plant_detection_sample.webp" + }, + { + "name": "GrapesNet_single_cluster", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Yelavi, Sangli, Maharashtra" + ], + "lat_lon": [ + "17.0408, 74.5126" + ], + "country": "India", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [ + "Sonaka" + ], + "sensor_modality": "rgb-d", + "imaging_equipment": [ + "OnePlus 7 Mobile phone (48-megapixel, f/1.7, 1.6-micron), tripod-mounted", + "Intel Real-Sense D435I Depth Camera (1280 x 720 resolution), Raspberry Pi 4 processing unit, tripod-mounted" + ], + "collection_period": "", + "platform": "tripod", + "input_data_format": "parquet", + "annotation_format": "none", + "num_images": 4312, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109100", + "citation": "Barbole, Dhanashree; Jadhav, Parul (2023), “GrapesNet: Indian Grape Clusters RGB & RGB-D Image Datasets”, Mendeley Data, V1, doi: 10.17632/mhzmzd5cwx.1", + "bibtex": "@article{barbole2023grapesnet,\n title={GrapesNet: Indian RGB & RGB-D vineyard image datasets for deep learning applications},\n author={Barbole, Dhanashree K. and Jadhav, Parul M.},\n journal={Data in Brief},\n volume={48},\n pages={109100},\n year={2023},\n publisher={Elsevier}\n}", + "zip_size_bytes": 220980797, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/GrapesNet_single_cluster", + "examples_image_url": "/img/agml/sample_images/GrapesNet_single_cluster_sample.webp" + }, + { + "name": "GrapesNet_multiple_cluster", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Yelavi, Sangli, Maharashtra" + ], + "lat_lon": [ + "17.0408, 74.5126" + ], + "country": "India", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [ + "Sonaka" + ], + "sensor_modality": "rgb-d", + "imaging_equipment": [ + "OnePlus 7 Mobile phone (48-megapixel, f/1.7, 1.6-micron), tripod-mounted", + "Intel Real-Sense D435I Depth Camera (1280 x 720 resolution), Raspberry Pi 4 processing unit, tripod-mounted" + ], + "collection_period": "", + "platform": "tripod", + "input_data_format": "parquet", + "annotation_format": "none", + "num_images": 2960, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109100", + "citation": "Barbole, Dhanashree; Jadhav, Parul (2023), “GrapesNet: Indian Grape Clusters RGB & RGB-D Image Datasets”, Mendeley Data, V1, doi: 10.17632/mhzmzd5cwx.1", + "bibtex": "@article{barbole2023grapesnet,\n title={GrapesNet: Indian RGB & RGB-D vineyard image datasets for deep learning applications},\n author={Barbole, Dhanashree K. and Jadhav, Parul M.},\n journal={Data in Brief},\n volume={48},\n pages={109100},\n year={2023},\n publisher={Elsevier}\n}", + "zip_size_bytes": 279070642, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/GrapesNet_multiple_cluster", + "examples_image_url": "/img/agml/sample_images/GrapesNet_multiple_cluster_sample.webp" + }, + { + "name": "GrapesNet_depth", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_detection", + "location": [ + "Yelavi, Sangli, Maharashtra" + ], + "lat_lon": [ + "17.0408, 74.5126" + ], + "country": "India", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [ + "Sonaka" + ], + "sensor_modality": "rgb-d", + "imaging_equipment": [ + "OnePlus 7 Mobile phone (48-megapixel, f/1.7, 1.6-micron), tripod-mounted", + "Intel Real-Sense D435I Depth Camera (1280 x 720 resolution), Raspberry Pi 4 processing unit, tripod-mounted" + ], + "collection_period": "", + "platform": "tripod", + "input_data_format": "parquet", + "annotation_format": "none", + "num_images": 847, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109100", + "citation": "Barbole, Dhanashree; Jadhav, Parul (2023), “GrapesNet: Indian Grape Clusters RGB & RGB-D Image Datasets”, Mendeley Data, V1, doi: 10.17632/mhzmzd5cwx.1", + "bibtex": "@article{barbole2023grapesnet,\n title={GrapesNet: Indian RGB & RGB-D vineyard image datasets for deep learning applications},\n author={Barbole, Dhanashree K. and Jadhav, Parul M.},\n journal={Data in Brief},\n volume={48},\n pages={109100},\n year={2023},\n publisher={Elsevier}\n}", + "zip_size_bytes": 886965320, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/GrapesNet_depth", + "examples_image_url": "/img/agml/sample_images/GrapesNet_depth_sample.webp" + }, + { + "name": "GrapesNet_single_cluster_depth", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_detection", + "location": [ + "Yelavi, Sangli, Maharashtra" + ], + "lat_lon": [ + "17.0408, 74.5126" + ], + "country": "India", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [ + "Sonaka" + ], + "sensor_modality": "rgb-d", + "imaging_equipment": [ + "OnePlus 7 Mobile phone (48-megapixel, f/1.7, 1.6-micron), tripod-mounted", + "Intel Real-Sense D435I Depth Camera (1280 x 720 resolution), Raspberry Pi 4 processing unit, tripod-mounted" + ], + "collection_period": "", + "platform": "tripod", + "input_data_format": "parquet", + "annotation_format": "none", + "num_images": 696, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109100", + "citation": "Barbole, Dhanashree; Jadhav, Parul (2023), “GrapesNet: Indian Grape Clusters RGB & RGB-D Image Datasets”, Mendeley Data, V1, doi: 10.17632/mhzmzd5cwx.1", + "bibtex": "@article{barbole2023grapesnet,\n title={GrapesNet: Indian RGB & RGB-D vineyard image datasets for deep learning applications},\n author={Barbole, Dhanashree K. and Jadhav, Parul M.},\n journal={Data in Brief},\n volume={48},\n pages={109100},\n year={2023},\n publisher={Elsevier}\n}", + "zip_size_bytes": 664320817, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/GrapesNet_single_cluster_depth", + "examples_image_url": "/img/agml/sample_images/GrapesNet_single_cluster_depth_sample.webp" + }, + { + "name": "medicinal_plant_classification_bd", + "machine_learning_task": "image_classification", + "agricultural_task": "crop_classification", + "location": [ + "Pharmacy Garden, Sirajganj, Bangladesh", + "Khwaja Yunus Ali Medical College & Hospital, Sirajganj, Bangladesh" + ], + "lat_lon": [], + "country": "Bangladesh", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "Nayantara", + "Pathor kuchi", + "Longevity spinach", + "Bohera", + "Haritaki", + "Thankuni", + "Neem", + "Tulsi", + "Lemon grass", + "Devil backbone" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Redmi Note 8, 13 megapixel, 409ppi, 1080x2340, handheld", + "Xiaomi 7, 8 megapixel, 269ppi, 720x1520, handheld", + "Samsung Galaxy A51, 32 megapixel, 405ppi, 1080x2400, handheld" + ], + "collection_period": "July to August", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 5000, + "classes": [ + "Bohera", + "Devilbackbone", + "Haritoki", + "Lemongrass", + "Nayontara", + "Neem", + "Pathorkuchi", + "Thankuni", + "Tulsi", + "Zenora" + ], + "license": "cc-by-nc-sa-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109211", + "citation": "Bijly Borkatullah, Jannatul Ferdous, Abdul Hasib Uddin, and Prince Mahmud. (2022). Medicinal Plant Raw [Dataset]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/4510170", + "bibtex": "@article{borkatulla2023bangladeshi,\n title={Bangladeshi medicinal plant dataset},\n author={Borkatulla, Bijly and Ferdous, Jannatul and Uddin, Abdul Hasib and Mahmud, Prince},\n journal={Data in Brief},\n volume={48},\n pages={109211},\n year={2023},\n publisher={Elsevier}\n}", + "zip_size_bytes": 10176004709, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/medicinal_plant_classification_bd", + "examples_image_url": "/img/agml/sample_images/medicinal_plant_classification_bd_sample.webp" + }, + { + "name": "DeepRootLab_root_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "root_segmentation", + "location": [ + "University of Copenhagen, Taastrup, Denmark" + ], + "lat_lon": [ + "55 40, 12 18" + ], + "country": "Denmark", + "environment": "", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 438, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1111/nph.71065", + "citation": "Han, E., Clément, C., Czaban, W., Smith, A. G., Dresbøll, D. B., & Thorup-Kristensen, K. (2025). Dataset used in 'Five seasons with DeepRootLab: A unique facility for easier deep root research in the field' [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.15213661", + "bibtex": "@article{han2026deep,\n title={Deep roots through time and crops: insight from five seasons at DeepRootLab},\n author={Han, Eusun and Clement, Corentin and Czaban, Weronika and Smith, Abraham George and Dresboll, Dorte Bodin and Thorup-Kristensen, Kristian},\n journal={New Phytologist},\n volume={250},\n number={4},\n pages={2670--2688},\n year={2026},\n publisher={Wiley Online Library}}", + "zip_size_bytes": 69952405, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/DeepRootLab_root_segmentation", + "examples_image_url": "/img/agml/sample_images/DeepRootLab_root_segmentation_sample.webp" + }, + { + "name": "chicory_root_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "root_segmentation", + "location": [ + "University of Copenhagen, Taastrup, Denmark" + ], + "lat_lon": [], + "country": "Denmark", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "chicory" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "2016", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 48, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1186/s13007-020-0563-0", + "citation": "Smith, A. G., Petersen, J., Selvan, R., & Rasmussen, C. R. (2019). Data for paper 'Segmentation of Roots in Soil with U-Net' [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.3527713", + "bibtex": "@article{smith2020segmentation,\n title={Segmentation of roots in soil with U-Net},\n author={Smith, Abraham George and Petersen, Jens and Selvan, Raghavendra and Rasmussen, Camilla Ruo},\n journal={Plant Methods},\n volume={16},\n number={1},\n pages={13},\n year={2020},\n publisher={Springer}}", + "zip_size_bytes": 420783965, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/chicory_root_segmentation", + "examples_image_url": "/img/agml/sample_images/chicory_root_segmentation_sample.webp" } ] diff --git a/static/img/agml/sample_images/CWF788_weed_segmentation_sample.webp b/static/img/agml/sample_images/CWF788_weed_segmentation_sample.webp new file mode 100644 index 0000000..68691c0 Binary files /dev/null and b/static/img/agml/sample_images/CWF788_weed_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/CottonWeedID15_classification_sample.webp b/static/img/agml/sample_images/CottonWeedID15_classification_sample.webp new file mode 100644 index 0000000..6afd7f3 Binary files /dev/null and b/static/img/agml/sample_images/CottonWeedID15_classification_sample.webp differ diff --git a/static/img/agml/sample_images/DeepFruits_classification_sample.webp b/static/img/agml/sample_images/DeepFruits_classification_sample.webp new file mode 100644 index 0000000..cc1578d Binary files /dev/null and b/static/img/agml/sample_images/DeepFruits_classification_sample.webp differ diff --git a/static/img/agml/sample_images/DeepRootLab_root_segmentation_sample.webp b/static/img/agml/sample_images/DeepRootLab_root_segmentation_sample.webp new file mode 100644 index 0000000..5980dff Binary files /dev/null and b/static/img/agml/sample_images/DeepRootLab_root_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/DeepWeeds_classification_sample.webp b/static/img/agml/sample_images/DeepWeeds_classification_sample.webp new file mode 100644 index 0000000..02e2e21 Binary files /dev/null and b/static/img/agml/sample_images/DeepWeeds_classification_sample.webp differ diff --git a/static/img/agml/sample_images/Dhan-Shomadhan_rice_leaf_disease_classification_sample.webp b/static/img/agml/sample_images/Dhan-Shomadhan_rice_leaf_disease_classification_sample.webp new file mode 100644 index 0000000..d737fc5 Binary files /dev/null and b/static/img/agml/sample_images/Dhan-Shomadhan_rice_leaf_disease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/EuroSat_LULC_classification_sample.webp b/static/img/agml/sample_images/EuroSat_LULC_classification_sample.webp new file mode 100644 index 0000000..8cbb816 Binary files /dev/null and b/static/img/agml/sample_images/EuroSat_LULC_classification_sample.webp differ diff --git a/static/img/agml/sample_images/EuroSat_multispectral_LULC_sample.webp b/static/img/agml/sample_images/EuroSat_multispectral_LULC_sample.webp new file mode 100644 index 0000000..442b01c Binary files /dev/null and b/static/img/agml/sample_images/EuroSat_multispectral_LULC_sample.webp differ diff --git a/static/img/agml/sample_images/EuroSat_rgb_LULC_sample.webp b/static/img/agml/sample_images/EuroSat_rgb_LULC_sample.webp new file mode 100644 index 0000000..d865b20 Binary files /dev/null and b/static/img/agml/sample_images/EuroSat_rgb_LULC_sample.webp differ diff --git a/static/img/agml/sample_images/GrapesNet_depth_sample.webp b/static/img/agml/sample_images/GrapesNet_depth_sample.webp new file mode 100644 index 0000000..8218c10 Binary files /dev/null and b/static/img/agml/sample_images/GrapesNet_depth_sample.webp differ diff --git a/static/img/agml/sample_images/GrapesNet_multiple_cluster_sample.webp b/static/img/agml/sample_images/GrapesNet_multiple_cluster_sample.webp new file mode 100644 index 0000000..422c73a Binary files /dev/null and b/static/img/agml/sample_images/GrapesNet_multiple_cluster_sample.webp differ diff --git a/static/img/agml/sample_images/GrapesNet_single_cluster_depth_sample.webp b/static/img/agml/sample_images/GrapesNet_single_cluster_depth_sample.webp new file mode 100644 index 0000000..a999425 Binary files /dev/null and b/static/img/agml/sample_images/GrapesNet_single_cluster_depth_sample.webp differ diff --git a/static/img/agml/sample_images/GrapesNet_single_cluster_sample.webp b/static/img/agml/sample_images/GrapesNet_single_cluster_sample.webp new file mode 100644 index 0000000..05a14e6 Binary files /dev/null and b/static/img/agml/sample_images/GrapesNet_single_cluster_sample.webp differ diff --git a/static/img/agml/sample_images/Musa_banana_tier_classification_sample.webp b/static/img/agml/sample_images/Musa_banana_tier_classification_sample.webp new file mode 100644 index 0000000..0ad2698 Binary files /dev/null and b/static/img/agml/sample_images/Musa_banana_tier_classification_sample.webp differ diff --git a/static/img/agml/sample_images/OPPD_plant_detection_sample.webp b/static/img/agml/sample_images/OPPD_plant_detection_sample.webp new file mode 100644 index 0000000..922840e Binary files /dev/null and b/static/img/agml/sample_images/OPPD_plant_detection_sample.webp differ diff --git a/static/img/agml/sample_images/PhenoBench_segmentation_sample.webp b/static/img/agml/sample_images/PhenoBench_segmentation_sample.webp new file mode 100644 index 0000000..18b3a6a Binary files /dev/null and b/static/img/agml/sample_images/PhenoBench_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/Sesame_Aerial_weed_segmentation_sample.webp b/static/img/agml/sample_images/Sesame_Aerial_weed_segmentation_sample.webp new file mode 100644 index 0000000..8d89b78 Binary files /dev/null and b/static/img/agml/sample_images/Sesame_Aerial_weed_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/WE3DS_weed_segmentation_sample.webp b/static/img/agml/sample_images/WE3DS_weed_segmentation_sample.webp new file mode 100644 index 0000000..efe865c Binary files /dev/null and b/static/img/agml/sample_images/WE3DS_weed_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/WPDv2_BBCH_classification_sample.webp b/static/img/agml/sample_images/WPDv2_BBCH_classification_sample.webp new file mode 100644 index 0000000..f671f95 Binary files /dev/null and b/static/img/agml/sample_images/WPDv2_BBCH_classification_sample.webp differ diff --git a/static/img/agml/sample_images/WeedsGalore_maize_segmentation_sample.webp b/static/img/agml/sample_images/WeedsGalore_maize_segmentation_sample.webp new file mode 100644 index 0000000..26f95a7 Binary files /dev/null and b/static/img/agml/sample_images/WeedsGalore_maize_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/carrot_weed_segmentation_sample.webp b/static/img/agml/sample_images/carrot_weed_segmentation_sample.webp new file mode 100644 index 0000000..c5d4b9d Binary files /dev/null and b/static/img/agml/sample_images/carrot_weed_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/chicory_root_segmentation_sample.webp b/static/img/agml/sample_images/chicory_root_segmentation_sample.webp new file mode 100644 index 0000000..0f7fce2 Binary files /dev/null and b/static/img/agml/sample_images/chicory_root_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/early-crop-weed_classification_sample.webp b/static/img/agml/sample_images/early-crop-weed_classification_sample.webp new file mode 100644 index 0000000..c7f9e3c Binary files /dev/null and b/static/img/agml/sample_images/early-crop-weed_classification_sample.webp differ diff --git a/static/img/agml/sample_images/medicinal_plant_classification_bd_sample.webp b/static/img/agml/sample_images/medicinal_plant_classification_bd_sample.webp new file mode 100644 index 0000000..e5d3dff Binary files /dev/null and b/static/img/agml/sample_images/medicinal_plant_classification_bd_sample.webp differ diff --git a/static/img/agml/sample_images/onion_weed_segmentation_sample.webp b/static/img/agml/sample_images/onion_weed_segmentation_sample.webp new file mode 100644 index 0000000..62f0597 Binary files /dev/null and b/static/img/agml/sample_images/onion_weed_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/pepper_disease_pest_classification_sample.webp b/static/img/agml/sample_images/pepper_disease_pest_classification_sample.webp new file mode 100644 index 0000000..1fd8297 Binary files /dev/null and b/static/img/agml/sample_images/pepper_disease_pest_classification_sample.webp differ