diff --git a/scripts/convert_to_webp.py b/scripts/convert_to_webp.py new file mode 100644 index 0000000..9889792 --- /dev/null +++ b/scripts/convert_to_webp.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 +""" +Convert PNG sample images to WebP and update JSON dataset manifests. + +Usage: + python3 scripts/convert_to_webp.py [--quality 85] [--dry-run] [--keep-originals] + +Converts every .png in static/img/agml/sample_images/ to .webp at the same +dimensions, then rewrites examples_image_url in both JSON manifests so .png +paths become .webp paths. +""" + +import argparse +import json +import os +import sys +import time +from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path + +try: + from PIL import Image +except ImportError: + sys.exit("Pillow is required: pip install Pillow") + +REPO_ROOT = Path(__file__).resolve().parent.parent +IMAGES_DIR = REPO_ROOT / "static" / "img" / "agml" / "sample_images" +DATA_DIR = REPO_ROOT / "static" / "data" +MANIFESTS = [DATA_DIR / "datasets.json", DATA_DIR / "hf_datasets.json"] + + +def convert_one(args: tuple[Path, int]) -> tuple[str, int, int, str | None]: + """Worker: convert a single PNG to WebP. Returns (filename, old_bytes, new_bytes, error).""" + png_path, quality = args + webp_path = png_path.with_suffix(".webp") + old_size = png_path.stat().st_size + try: + with Image.open(png_path) as img: + img.save(webp_path, "WEBP", quality=quality, method=6) + return png_path.name, old_size, webp_path.stat().st_size, None + except Exception as exc: + if webp_path.exists(): + webp_path.unlink() + return png_path.name, old_size, 0, str(exc) + + +def update_manifest(path: Path, dry_run: bool) -> int: + """Replace .png with .webp in examples_image_url fields. Returns number of entries changed.""" + raw = path.read_text(encoding="utf-8") + data = json.loads(raw) + + changed = 0 + + def patch(obj): + nonlocal changed + if isinstance(obj, dict): + for key, value in obj.items(): + if key == "examples_image_url" and isinstance(value, str) and value.endswith(".png"): + obj[key] = value[:-4] + ".webp" + changed += 1 + else: + patch(value) + elif isinstance(obj, list): + for item in obj: + patch(item) + + patch(data) + + if not dry_run and changed: + path.write_text(json.dumps(data, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") + + return changed + + +def main(): + parser = argparse.ArgumentParser(description="Convert PNG sample images to WebP.") + parser.add_argument("--quality", type=int, default=85, help="WebP quality 1-100 (default 85)") + parser.add_argument("--dry-run", action="store_true", help="Report what would happen without writing files") + parser.add_argument("--keep-originals", action="store_true", help="Keep original PNG files after conversion") + parser.add_argument("--workers", type=int, default=os.cpu_count(), help="Parallel worker processes") + args = parser.parse_args() + + pngs = sorted(IMAGES_DIR.glob("*.png")) + if not pngs: + print(f"No PNG files found in {IMAGES_DIR}") + return + + print(f"Found {len(pngs)} PNG files in {IMAGES_DIR.relative_to(REPO_ROOT)}") + print(f"Quality: {args.quality} Workers: {args.workers} Dry-run: {args.dry_run}\n") + + if args.dry_run: + print("[dry-run] Skipping conversion — would convert the following:") + for png in pngs: + print(f" {png.name}") + print() + else: + total_old = 0 + total_new = 0 + errors = [] + start = time.perf_counter() + + tasks = [(png, args.quality) for png in pngs] + + with ProcessPoolExecutor(max_workers=args.workers) as pool: + futures = {pool.submit(convert_one, task): task[0] for task in tasks} + completed = 0 + for future in as_completed(futures): + name, old_bytes, new_bytes, error = future.result() + completed += 1 + if error: + errors.append((name, error)) + print(f" [{completed:>3}/{len(pngs)}] ERROR {name}: {error}") + else: + ratio = (1 - new_bytes / old_bytes) * 100 if old_bytes else 0 + total_old += old_bytes + total_new += new_bytes + print(f" [{completed:>3}/{len(pngs)}] {name[:-4]}.webp {old_bytes/1e6:.1f}MB → {new_bytes/1e6:.1f}MB (-{ratio:.0f}%)") + + elapsed = time.perf_counter() - start + overall = (1 - total_new / total_old) * 100 if total_old else 0 + print(f"\nConverted {len(pngs) - len(errors)}/{len(pngs)} images in {elapsed:.1f}s") + print(f"Total: {total_old/1e6:.1f} MB → {total_new/1e6:.1f} MB (-{overall:.0f}%)") + + if not args.keep_originals: + removed = 0 + for png in pngs: + webp = png.with_suffix(".webp") + if webp.exists(): + png.unlink() + removed += 1 + print(f"Removed {removed} original PNG files") + + if errors: + print(f"\n{len(errors)} conversion(s) failed:") + for name, err in errors: + print(f" {name}: {err}") + + print("\nUpdating JSON manifests...") + for manifest in MANIFESTS: + if not manifest.exists(): + print(f" SKIP {manifest.name} (not found)") + continue + changed = update_manifest(manifest, args.dry_run) + tag = "[dry-run] would update" if args.dry_run else "Updated" + print(f" {tag} {manifest.name}: {changed} entries changed") + + print("\nDone.") + + +if __name__ == "__main__": + main() diff --git a/static/data/datasets.json b/static/data/datasets.json index 0e9ee83..14f1ca1 100644 --- a/static/data/datasets.json +++ b/static/data/datasets.json @@ -22,7 +22,7 @@ 0.247, 0.24 ], - "examples_image_url": "/img/agml/sample_images/almond_bloom_2023_examples.png", + "examples_image_url": "/img/agml/sample_images/almond_bloom_2023_examples.webp", "source": "agml", "license": "Apache 2.0", "citation": "", @@ -53,7 +53,7 @@ 0.17, 0.167 ], - "examples_image_url": "/img/agml/sample_images/almond_harvest_2021_examples.png", + "examples_image_url": "/img/agml/sample_images/almond_harvest_2021_examples.webp", "source": "agml", "license": "Apache 2.0", "citation": "", @@ -84,7 +84,7 @@ 0.187, 0.173 ], - "examples_image_url": "/img/agml/sample_images/apple_detection_drone_brazil_examples.png", + "examples_image_url": "/img/agml/sample_images/apple_detection_drone_brazil_examples.webp", "source": "agml", "license": "CC BY-SA 4.0", "citation": "@article{DBLP:journals/corr/abs-2110-12331,\n author = {Thiago T. Santos and\n Luciano Gebler},\n title = {A methodology for detection and localization of fruits in apples orchards\n from aerial images},\n journal = {CoRR},\n volume = {abs/2110.12331},\n year = {2021},\n url = {https://arxiv.org/abs/2110.12331},\n eprinttype = {arXiv},\n eprint = {2110.12331},\n timestamp = {Thu, 28 Oct 2021 15:25:31 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2110-12331.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}", @@ -115,7 +115,7 @@ 0.28, 0.283 ], - "examples_image_url": "/img/agml/sample_images/apple_detection_spain_examples.png", + "examples_image_url": "/img/agml/sample_images/apple_detection_spain_examples.webp", "source": "agml", "license": "", "citation": "@article{GENEMOLA2019104289,\ntitle = {KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data},\njournal = {Data in Brief},\nvolume = {25},\npages = {104289},\nyear = {2019},\nissn = {2352-3409},\ndoi = {https://doi.org/10.1016/j.dib.2019.104289},\nurl = {https://www.sciencedirect.com/science/article/pii/S2352340919306432},\nauthor = {Jordi Gené-Mola and Verónica Vilaplana and Joan R. Rosell-Polo and Josep-Ramon Morros and Javier Ruiz-Hidalgo and Eduard Gregorio},\nkeywords = {Multi-modal dataset, Fruit detection, Depth cameras, RGB-D, Fruit reflectance, Fuji apple},\nabstract = {This article contains data related to the research article entitle “Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities” [1]. The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. RGB-D sensors have shown potential for fruit detection and localization since they provide 3D information with color data. However, the lack of substantial datasets is a barrier for exploiting the use of these sensors. This article presents the KFuji RGB-DS database which is composed by 967 multi-modal images of Fuji apples on trees captured using Microsoft Kinect v2 (Microsoft, Redmond, WA, USA). Each image contains information from 3 different modalities: color (RGB), depth (D) and range corrected IR intensity (S). Ground truth fruit locations were manually annotated, labeling a total of 12,839 apples in all the dataset. The current dataset is publicly available at http://www.grap.udl.cat/publicacions/datasets.html.}\n}", @@ -146,7 +146,7 @@ 0.186, 0.189 ], - "examples_image_url": "/img/agml/sample_images/apple_detection_usa_examples.png", + "examples_image_url": "/img/agml/sample_images/apple_detection_usa_examples.webp", "source": "agml", "license": "", "citation": "@article{karkee2019apple,\n title={Apple Dataset Benchmark from Orchard Environment in Modern Fruiting Wall},\n author={Karkee, Manoj and Bhusal, Santosh and Zhang, Qin},\n year={2019}\n}", @@ -177,7 +177,7 @@ 0.193, 0.216 ], - "examples_image_url": "/img/agml/sample_images/apple_flower_segmentation_examples.png", + "examples_image_url": "/img/agml/sample_images/apple_flower_segmentation_examples.webp", "source": "agml", "license": "US Public Domain", "citation": "@ARTICLE{8392727,\n author={Dias, Philipe A. and Tabb, Amy and Medeiros, Henry},\n journal={IEEE Robotics and Automation Letters}, \n title={Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network}, \n year={2018},\n volume={3},\n number={4},\n pages={3003-3010},\n doi={10.1109/LRA.2018.2849498}}", @@ -208,7 +208,7 @@ 0.288, 0.331 ], - "examples_image_url": "/img/agml/sample_images/apple_segmentation_minnesota_examples.png", + "examples_image_url": "/img/agml/sample_images/apple_segmentation_minnesota_examples.webp", "source": "agml", "license": "MIT", "citation": "@misc{hani2019minneapple,\n title={MinneApple: A Benchmark Dataset for Apple Detection and Segmentation},\n author={Nicolai Häni and Pravakar Roy and Volkan Isler}\n year={2019},\n eprint={1909.06441},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}", @@ -239,7 +239,7 @@ 0.101, 0.139 ], - "examples_image_url": "/img/agml/sample_images/arabica_coffee_leaf_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/arabica_coffee_leaf_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "@article{JEPKOECH2021107142, title = {Arabica coffee leaf images dataset for coffee leaf disease detection and classification}, journal = {Data in Brief}, volume = {36}, pages = {107142}, year = {2021}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2021.107142}, url = {https://www.sciencedirect.com/science/article/pii/S2352340921004261}, author = {Jennifer Jepkoech and David Muchangi Mugo and Benson K. Kenduiywo and Edna Chebet Too}}", @@ -293,7 +293,7 @@ 0.204, 0.186 ], - "examples_image_url": "/img/agml/sample_images/banana_leaf_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/banana_leaf_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "hailu, yordanos (2021), “Banana Leaf Disease Images”, Mendeley Data, V1, doi: 10.17632/rjykr62kdh.1", @@ -324,7 +324,7 @@ 0.199, 0.169 ], - "examples_image_url": "/img/agml/sample_images/bean_disease_uganda_examples.png", + "examples_image_url": "/img/agml/sample_images/bean_disease_uganda_examples.webp", "source": "agml", "license": "MIT", "citation": "", @@ -347,7 +347,7 @@ "classes": "leaves, branches", "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/bean_synthetic_earlygrowth_aerial_examples.png", + "examples_image_url": "/img/agml/sample_images/bean_synthetic_earlygrowth_aerial_examples.webp", "source": "agml", "license": "MIT", "citation": "@ARTICLE{10.3389/fpls.2019.01185,\n \nAUTHOR={Bailey, Brian N.}, \n\t \nTITLE={Helios: A Scalable 3D Plant and Environmental Biophysical Modeling Framework}, \n\t\nJOURNAL={Frontiers in Plant Science}, \n\t\nVOLUME={10}, \n\t\nYEAR={2019}, \n\t \nURL={https://www.frontiersin.org/article/10.3389/fpls.2019.01185}, \n\t\nDOI={10.3389/fpls.2019.01185}, \n\t\nISSN={1664-462X}, \n \nABSTRACT={This article presents an overview of Helios, a new three-dimensional (3D) plant and environmental modeling framework. Helios is a model coupling framework designed to provide maximum flexibility in integrating and running arbitrary 3D environmental system models. Users interact with Helios through a well-documented open-source C++ API. Version 1.0 comes with model plug-ins for radiation transport, the surface energy balance, stomatal conductance, photosynthesis, solar position, and procedural tree generation. Additional plug-ins are also available for visualizing model geometry and data and for processing and integrating LiDAR scanning data. Many of the plug-ins perform calculations on the graphics processing unit, which allows for efficient simulation of very large domains with high detail. An example modeling study is presented in which leaf-level heterogeneity in water usage and photosynthesis of an orchard is examined to understand how this leaf-scale variability contributes to whole-tree and -canopy fluxes.}\n}", @@ -378,7 +378,7 @@ 0.197, 0.218 ], - "examples_image_url": "/img/agml/sample_images/betel_leaf_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/betel_leaf_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Rashid, Mohammad Rifat Ahmmad; Hossain, Md. Miskat ; Biswas, Joy ; Majumder, Hredoy (2024), “Betel Leaf Image Dataset from Bangladesh”, Mendeley Data, V2, doi: 10.17632/g7fpgj57wc.2", @@ -401,7 +401,7 @@ "classes": "anthracnose, healthy, leaf_crinckle, powdery_mildew, yellow_mosaic", "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/blackgram_plant_leaf_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/blackgram_plant_leaf_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Talasila, Srinivas; Rawal, Kirti; Sethi, Gaurav; MSS, Sanjay; M, Surya Prakash Reddy (2022), “Blackgram Plant Leaf Disease Dataset”, Mendeley Data, V3, doi: 10.17632/zfcv9fmrgv.3", @@ -432,7 +432,7 @@ 0.1, 0.086 ], - "examples_image_url": "/img/agml/sample_images/carrot_weeds_germany_examples.png", + "examples_image_url": "/img/agml/sample_images/carrot_weeds_germany_examples.webp", "source": "agml", "license": "", "citation": "@inproceedings{haug15,\n author={Haug, Sebastian and Ostermann, J{\\\"o}rn},\n title={A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks},\n year={2015},\n booktitle={Computer Vision - ECCV 2014 Workshops},\n doi={10.1007/978-3-319-16220-1_8},\n url={http://dx.doi.org/10.1007/978-3-319-16220-1_8},\n pages={105--116}\n}", @@ -463,7 +463,7 @@ 0.177, 0.182 ], - "examples_image_url": "/img/agml/sample_images/chilli_leaf_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/chilli_leaf_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Aishwarya, M.P & Reddy, A.. (2024). Dataset of Chilli and Onion Plant Leaf Images for Classification and Detection. Data in Brief. 54. 110524. 10.1016/j.dib.2024.110524. ", @@ -494,7 +494,7 @@ 0.208, 0.22 ], - "examples_image_url": "/img/agml/sample_images/coconut_tree_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/coconut_tree_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "PATIL, Kailas; Thite, Sandip; Suryawanshi, Yogesh; chumchu, prawit (2023), “Coconut Tree Disease Dataset”, Mendeley Data, V1, doi: 10.17632/gh56wbsnj5.1", @@ -525,7 +525,7 @@ 0.167, 0.174 ], - "examples_image_url": "/img/agml/sample_images/corn_maize_leaf_disease_examples.png", + "examples_image_url": "/img/agml/sample_images/corn_maize_leaf_disease_examples.webp", "source": "agml", "license": "", "citation": "Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N. PlantDoc: a dataset for visual plant disease detection. InProceedings of the 7th ACM IKDD CoDS and 25th COMAD 2020 Jan 5 (pp. 249-253).", @@ -557,7 +557,7 @@ 0.153, 0.138 ], - "examples_image_url": "/img/agml/sample_images/crop_weeds_greece_examples.png", + "examples_image_url": "/img/agml/sample_images/crop_weeds_greece_examples.webp", "source": "agml", "license": "MIT", "citation": "@article{ESPEJOGARCIA2020105306,\n title = {Towards weeds identification assistance through transfer learning},\n journal = {Computers and Electronics in Agriculture},\n volume = {171},\n pages = {105306},\n year = {2020},\n issn = {0168-1699},\n doi = {https://doi.org/10.1016/j.compag.2020.105306},\n url = {https://www.sciencedirect.com/science/article/pii/S0168169919319854},\n author = {Borja Espejo-Garcia and Nikos Mylonas and Loukas Athanasakos and Spyros Fountas and Ioannis Vasilakoglou},\n keywords = {Weed identification, Deep learning, Transfer learning, Open data, Precision agriculture},\n abstract = {Reducing the use of pesticides through selective spraying is an important component towards a more sustainable computer-assisted agriculture. Weed identification at early growth stage contributes to reduced herbicide rates. However, while computer vision alongside deep learning have overcome the performance of approaches that use hand-crafted features, there are still some open challenges in the development of a reliable automatic plant identification system. These type of systems have to take into account different sources of variability, such as growth stages and soil conditions, with the added constraint of the limited size of usual datasets. This study proposes a novel crop/weed identification system that relies on a combination of fine-tuning pre-trained convolutional networks (Xception, Inception-Resnet, VGNets, Mobilenet and Densenet) with the “traditional” machine learning classifiers (Support Vector Machines, XGBoost and Logistic Regression) trained with the previously deep extracted features. The aim of this approach was to avoid overfitting and to obtain a robust and consistent performance. To evaluate this approach, an open access dataset of two crop [tomato (Solanum lycopersicum L.) and cotton (Gossypium hirsutum L.)] and two weed species [black nightshade (Solanum nigrum L.) and velvetleaf (Abutilon theophrasti Medik.)] was generated. The pictures were taken by different production sites across Greece under natural variable light conditions from RGB cameras. The results revealed that a combination of fine-tuned Densenet and Support Vector Machine achieved a micro F1 score of 99.29% with a very low performance difference between train and test sets. Other evaluated approaches also obtained repeatedly more than 95% F1 score. Additionally, our results analysis provides some heuristics for designing transfer-learning based systems to avoid overfitting without decreasing performance.}\n}", @@ -589,7 +589,7 @@ 0.184, 0.179 ], - "examples_image_url": "/img/agml/sample_images/cucumber_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/cucumber_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Sultana, Nusrat; Shorif, Sumaita Binte ; Akter, Morium ; Uddin, Mohammad Shorif (2022), “Cucumber Disease Recognition Dataset”, Mendeley Data, V1, doi: 10.17632/y6d3z6f8z9.1", @@ -620,7 +620,7 @@ 0.212, 0.222 ], - "examples_image_url": "/img/agml/sample_images/embrapa_wgisd_grape_detection_examples.png", + "examples_image_url": "/img/agml/sample_images/embrapa_wgisd_grape_detection_examples.webp", "source": "agml", "license": "CC BY-NC 4.0", "citation": "", @@ -651,7 +651,7 @@ 0.225, 0.218 ], - "examples_image_url": "/img/agml/sample_images/fruit_detection_worldwide_examples.png", + "examples_image_url": "/img/agml/sample_images/fruit_detection_worldwide_examples.webp", "source": "agml", "license": "", "citation": "@Article{s16081222,\n AUTHOR = {Sa, Inkyu and Ge, Zongyuan and Dayoub, Feras and Upcroft, Ben and Perez, Tristan and McCool, Chris},\n TITLE = {DeepFruits: A Fruit Detection System Using Deep Neural Networks},\n JOURNAL = {Sensors},\n VOLUME = {16},\n YEAR = {2016},\n NUMBER = {8},\n ARTICLE-NUMBER = {1222},\n URL = {https://www.mdpi.com/1424-8220/16/8/1222},\n ISSN = {1424-8220},\n ABSTRACT = {This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0 . 807 to 0 . 838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.},\n DOI = {10.3390/s16081222}\n}", @@ -688,7 +688,7 @@ 0.152, 0.145 ], - "examples_image_url": "/img/agml/sample_images/gemini_flower_detection_2022_examples.png", + "examples_image_url": "/img/agml/sample_images/gemini_flower_detection_2022_examples.webp", "source": "agml", "license": "Apache 2.0", "citation": "", @@ -719,7 +719,7 @@ 0.153, 0.132 ], - "examples_image_url": "/img/agml/sample_images/gemini_leaf_detection_2022_examples.png", + "examples_image_url": "/img/agml/sample_images/gemini_leaf_detection_2022_examples.webp", "source": "agml", "license": "Apache 2.0", "citation": "", @@ -750,7 +750,7 @@ 0.152, 0.13 ], - "examples_image_url": "/img/agml/sample_images/gemini_plant_detection_2022_examples.png", + "examples_image_url": "/img/agml/sample_images/gemini_plant_detection_2022_examples.webp", "source": "agml", "license": "Apache 2.0", "citation": "", @@ -781,7 +781,7 @@ 0.161, 0.165 ], - "examples_image_url": "/img/agml/sample_images/gemini_pod_detection_2022_examples.png", + "examples_image_url": "/img/agml/sample_images/gemini_pod_detection_2022_examples.webp", "source": "agml", "license": "Apache 2.0", "citation": "", @@ -804,7 +804,7 @@ "classes": "canopy, crown", "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/ghai_broccoli_detection_examples.png", + "examples_image_url": "/img/agml/sample_images/ghai_broccoli_detection_examples.webp", "source": "agml", "license": "CC BY-SA 4.0", "citation": "", @@ -896,7 +896,7 @@ "classes": "Bud, Calyx, Detached Fruit, Flower, Large green, Leaf, Ripe fruit, Small Green, Stem, Unripe fruit", "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/ghai_strawberry_fruit_detection_examples.png", + "examples_image_url": "/img/agml/sample_images/ghai_strawberry_fruit_detection_examples.webp", "source": "agml", "license": "CC BY-SA 4.0", "citation": "", @@ -927,7 +927,7 @@ 0.259, 0.263 ], - "examples_image_url": "/img/agml/sample_images/grape_detection_californiaday_examples.png", + "examples_image_url": "/img/agml/sample_images/grape_detection_californiaday_examples.webp", "source": "agml", "license": "", "citation": "@misc{GrapeDay,\n author = {Plant AI and Biophysics Lab},\n title = {Grape Detection 2019 Day},\n year = {2019},\n url = {https://github.com/plant-ai-biophysics-lab/AgML} \n ", @@ -958,7 +958,7 @@ 0.201, 0.206 ], - "examples_image_url": "/img/agml/sample_images/grape_detection_californianight_examples.png", + "examples_image_url": "/img/agml/sample_images/grape_detection_californianight_examples.webp", "source": "agml", "license": "", "citation": "@misc{GrapeNight,\n author = {Plant AI and Biophysics Lab},\n title = {Grape Detection 2020 Night},\n year = {2020},\n url = {https://github.com/plant-ai-biophysics-lab/AgML} \n ", @@ -989,7 +989,7 @@ 0.186, 0.224 ], - "examples_image_url": "/img/agml/sample_images/grape_detection_syntheticday_examples.png", + "examples_image_url": "/img/agml/sample_images/grape_detection_syntheticday_examples.webp", "source": "agml", "license": "", "citation": "@ARTICLE{10.3389/fpls.2019.01185,\n \nAUTHOR={Bailey, Brian N.}, \n\t \nTITLE={Helios: A Scalable 3D Plant and Environmental Biophysical Modeling Framework}, \n\t\nJOURNAL={Frontiers in Plant Science}, \n\t\nVOLUME={10}, \n\t\nYEAR={2019}, \n\t \nURL={https://www.frontiersin.org/article/10.3389/fpls.2019.01185}, \n\t\nDOI={10.3389/fpls.2019.01185}, \n\t\nISSN={1664-462X}, \n \nABSTRACT={This article presents an overview of Helios, a new three-dimensional (3D) plant and environmental modeling framework. Helios is a model coupling framework designed to provide maximum flexibility in integrating and running arbitrary 3D environmental system models. Users interact with Helios through a well-documented open-source C++ API. Version 1.0 comes with model plug-ins for radiation transport, the surface energy balance, stomatal conductance, photosynthesis, solar position, and procedural tree generation. Additional plug-ins are also available for visualizing model geometry and data and for processing and integrating LiDAR scanning data. Many of the plug-ins perform calculations on the graphics processing unit, which allows for efficient simulation of very large domains with high detail. An example modeling study is presented in which leaf-level heterogeneity in water usage and photosynthesis of an orchard is examined to understand how this leaf-scale variability contributes to whole-tree and -canopy fluxes.}\n}", @@ -1012,7 +1012,7 @@ "classes": "maskLeaves, maskPlants, maskStems, maskVoid", "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/growliflower_cauliflower_segmentation_examples.png", + "examples_image_url": "/img/agml/sample_images/growliflower_cauliflower_segmentation_examples.webp", "source": "agml", "license": "", "citation": "Kierdorf, Jana & Junker-Frohn, Laura & Delaney, Mike & Olave, Mariele & Burkart, Andreas & Jaenicke, Hannah & Muller, Onno & Roscher, Ribana. (2022). GrowliFlower: An image time‐series dataset for GROWth analysis of cauLIFLOWER. Journal of Field Robotics. 40. 10.1002/rob.22122. ", @@ -1043,7 +1043,7 @@ 0.184, 0.187 ], - "examples_image_url": "/img/agml/sample_images/guava_disease_pakistan_examples.png", + "examples_image_url": "/img/agml/sample_images/guava_disease_pakistan_examples.webp", "source": "agml", "license": "", "citation": "@article{Rauf_Lali_2021, \n title={A Guava Fruits and Leaves Dataset for Detection and Classification of Guava Diseases through Machine Learning}, \n volume={1}, \n url={https://data.mendeley.com/datasets/s8x6jn5cvr/1}, \n DOI={10.17632/s8x6jn5cvr.1}, \n abstractNote={(1) Plant diseases are the primary cause of reduced productivity in agriculture, which results in economic losses. Guava is a big source of nutrients for humans all over the world. Guava diseases, on the other hand, harm the yield and quality of the crop. (2) For the identification and classification of plant diseases, computer vision and image processing methods have been commonly used. (3) The dataset includes an image gallery of healthy and unhealthy Guava fruits and leaves that could be used by researchers to adopt advanced computer vision techniques to protect plants from disease. Dot, Canker, Mummification, and Rust are the diseases targeted in the data sets. (4) The dataset contains 306 images of healthy and unhealthy images for both Guava fruits and leaves collectively. Each image contains 6000 * 4000 dimensions with 300 dpi resolution. (5) All images were acquired from the tropical areas of Pakistan under the supervision of Prof. Dr. Ikramullah Lali. (6) All images were annotated manually by the domain expert such as For Guava fruits and leaves; Dot (76), Canker (77), Mummification (83), and Rust (70) Note: The data labeling was manual and can be updated by automatic labeling through machine learning. In the meantime, the authors can also use the data set for the clustering problem.}, \n author={Rauf, Hafiz Tayyab and Lali, Muhammad Ikram Ullah}, \n year={2021}, month={Apr} \n}\n", @@ -1066,7 +1066,7 @@ "classes": null, "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/iNatAg_sample_images.png", + "examples_image_url": "/img/agml/sample_images/iNatAg_sample_images.webp", "license": "CC BY-NC 4.0", "citation": "https://www.inaturalist.org/", "parent_dataset": null, @@ -1087,7 +1087,7 @@ "classes": null, "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/iNatAg_sample_images.png", + "examples_image_url": "/img/agml/sample_images/iNatAg_sample_images.webp", "license": "CC BY-NC 4.0", "citation": "https://www.inaturalist.org/", "parent_dataset": null, @@ -125352,7 +125352,7 @@ 0.208, 0.302 ], - "examples_image_url": "/img/agml/sample_images/java_plum_leaf_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/java_plum_leaf_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Bhowmik, Auvick Chandra; Ahad, Taimur (2024), “Java Plum Leaf Disease Dataset”, Mendeley Data, V3, doi: 10.17632/43d75vptz4.3", @@ -125383,7 +125383,7 @@ 0.143, 0.115 ], - "examples_image_url": "/img/agml/sample_images/leaf_counting_denmark_examples.png", + "examples_image_url": "/img/agml/sample_images/leaf_counting_denmark_examples.webp", "source": "agml", "license": "CC BY-SA 4.0", "citation": "@Article{s18051580,\n author = {Teimouri, Nima and Dyrmann, Mads and Nielsen, Per Rydahl and Mathiassen, Solvejg Kopp and Somerville, Gayle J. and Jørgensen, Rasmus Nyholm},\n title = {Weed Growth Stage Estimator Using Deep Convolutional Neural Networks},\n journal = {Sensors},\n volume = {18},\n year = {2018},\n number = {5},\n url = {http://www.mdpi.com/1424-8220/18/5/1580},\n issn = {1424-8220}\n}", @@ -125412,7 +125412,7 @@ 0.079, 0.043 ], - "examples_image_url": "/img/agml/sample_images/mango_detection_australia_examples.png", + "examples_image_url": "/img/agml/sample_images/mango_detection_australia_examples.webp", "source": "agml", "license": "", "citation": "@Misc{Koirala2019,\n author={Koirala, Anand and Walsh, Kerry and Wang, Z. and McCarthy, C.},\n title={MangoYOLO data set},\n year={2019},\n month={2021},\n day={10-19},\n publisher={Central Queensland University},\n keywords={Mango images; Fruit detection; Yield estimation; Mango; Agricultural Land Management; Horticultural Crop Growth and Development},\n abstract={Datasets and directories are structured similar to the PASCAL VOC dataset, avoiding the need to change scripts already available, with the detection frameworks ready to parse PASCAL VOC annotations into their format. The sub-directory JPEGImages consist of 1730 images (612x512 pixels) used for train, test and validation. Each image has at least one annotated fruit. The sub-directory Annotations consists of all the annotation files (record of bounding box coordinates for each image) in xml format and have the same name as the image name. The sub-directory Main consists of the text file that contains image names (without extension) used for train, test and validation. Training set (train.txt) lists 1300 train images Validation set (val.txt) lists 130 validation images Test set (test.txt) lists 300 test images Each image has an XML annotation file (filename = image name) and each image set (training validation and test set) has associated text files (train.txt, val.txt and test.txt) containing the list of image names to be used for training and testing. The XML annotation file contains the image attributes (name, width, height), the object attributes (class name, object bounding box co-ordinates (xmin, ymin, xmax, ymax)). (xmin, ymin) and (xmax, ymax) are the pixel co-ordinates of the bounding box's top-left corner and bottom-right corner respectively.},\n note={CC-BY-4.0},\n url={https://figshare.com/articles/dataset/MangoYOLO_data_set/13450661, https://researchdata.edu.au/mangoyolo-set},\n language={English}\n}", @@ -125443,7 +125443,7 @@ 0.186, 0.257 ], - "examples_image_url": "/img/agml/sample_images/mango_leaf_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/mango_leaf_disease_classification_examples.webp", "source": "agml", "license": "CC BY-NC 4.0", "citation": "Ali, Sawkat; Ibrahim, Muhammad ; Ahmed, Sarder Iftekhar ; Nadim, Md. ; Mizanur, Mizanur Rahman; Shejunti, Maria Mehjabin ; Jabid, Taskeed (2022), “MangoLeafBD Dataset”, Mendeley Data, V1, doi: 10.17632/hxsnvwty3r.1", @@ -125474,7 +125474,7 @@ 0.146, 0.157 ], - "examples_image_url": "/img/agml/sample_images/onion_leaf_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/onion_leaf_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Aishwarya, M.P & Reddy, A.. (2024). Dataset of Chilli and Onion Plant Leaf Images for Classification and Detection. Data in Brief. 54. 110524. 10.1016/j.dib.2024.110524. ", @@ -125505,7 +125505,7 @@ 0.196, 0.265 ], - "examples_image_url": "/img/agml/sample_images/orange_leaf_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/orange_leaf_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Emon, Yousuf Rayhan; Ahad, Md Taimur (2023), “Multi-format open-source sweet orange leaf dataset for disease detection, classification, and analysis.”, Mendeley Data, V1, doi: 10.17632/f7cr74mwpj.1", @@ -125536,7 +125536,7 @@ 0.233, 0.19 ], - "examples_image_url": "/img/agml/sample_images/paddy_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/paddy_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Petchiammal A, Briskline Kiruba S, Murugan D, Pandarasamy Arjunan. (2022). Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking. IEEE Dataport. https://dx.doi.org/10.21227/hz4v-af08", @@ -125559,7 +125559,7 @@ "classes": "Anthracnose, BacterialSpot, Curl, Healthy, RingSpot", "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/papaya_leaf_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/papaya_leaf_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Sarker, Arpita ; Mustofa, Sumaya; Ahad, Md Taimur (2023), “BDPapayaLeaf: A annotation based image dataset of papaya leaf disease.”, Mendeley Data, V1, doi: 10.17632/p997fvf526.1", @@ -125590,7 +125590,7 @@ 0.214, 0.244 ], - "examples_image_url": "/img/agml/sample_images/peachpear_flower_segmentation_examples.png", + "examples_image_url": "/img/agml/sample_images/peachpear_flower_segmentation_examples.webp", "source": "agml", "license": "US Public Domain", "citation": "@ARTICLE{8392727,\n author={Dias, Philipe A. and Tabb, Amy and Medeiros, Henry},\n journal={IEEE Robotics and Automation Letters}, \n title={Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network}, \n year={2018},\n volume={3},\n number={4},\n pages={3003-3010},\n doi={10.1109/LRA.2018.2849498}}", @@ -125622,7 +125622,7 @@ 0.204, 0.221 ], - "examples_image_url": "/img/agml/sample_images/plant_doc_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/plant_doc_classification_examples.webp", "source": "agml", "license": "CC BY-SA 4.0", "citation": "@inproceedings{10.1145/3371158.3371196,\n author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},\n title = {PlantDoc: A Dataset for Visual Plant Disease Detection},\n year = {2020},\n isbn = {9781450377386},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n url = {https://doi.org/10.1145/3371158.3371196},\n doi = {10.1145/3371158.3371196},\n booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},\n pages = {249–253},\n numpages = {5},\n keywords = {Deep Learning, Object Detection, Image Classification},\n location = {Hyderabad, India},\n series = {CoDS COMAD 2020}\n }", @@ -125651,7 +125651,7 @@ 0.204, 0.218 ], - "examples_image_url": "/img/agml/sample_images/plant_doc_detection_examples.png", + "examples_image_url": "/img/agml/sample_images/plant_doc_detection_examples.webp", "source": "agml", "license": "CC BY-SA 4.0", "citation": "@inproceedings{10.1145/3371158.3371196,\n author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},\n title = {PlantDoc: A Dataset for Visual Plant Disease Detection},\n year = {2020},\n isbn = {9781450377386},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n url = {https://doi.org/10.1145/3371158.3371196},\n doi = {10.1145/3371158.3371196},\n booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},\n pages = {249–253},\n numpages = {5},\n keywords = {Deep Learning, Object Detection, Image Classification},\n location = {Hyderabad, India},\n series = {CoDS COMAD 2020}\n }", @@ -125680,7 +125680,7 @@ 0.096, 0.106 ], - "examples_image_url": "/img/agml/sample_images/plant_seedlings_aarhus_examples.png", + "examples_image_url": "/img/agml/sample_images/plant_seedlings_aarhus_examples.webp", "source": "agml", "license": "CC BY-SA 4.0", "citation": "@article{Giselsson2017,\n author = {Giselsson, Thomas Mosgaard and Dyrmann, Mads and J{\\o}rgensen, Rasmus Nyholm and Jensen, Peter Kryger and Midtiby, Henrik Skov},\n journal = {arXiv preprint},\n keywords = {benchmark,database,plant seedlings,segmentation,site-specific weed control},\n title = {{A Public Image Database for Benchmark of Plant Seedling Classification Algorithms}},\n year = {2017}\n}", @@ -125709,7 +125709,7 @@ 0.152, 0.194 ], - "examples_image_url": "/img/agml/sample_images/plant_village_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/plant_village_classification_examples.webp", "source": "agml", "license": "", "citation": "@article{DBLP:journals/corr/HughesS15,\n author = {David P. Hughes and\n Marcel Salath{'{e} } },\n title = {An open access repository of images on plant health to enable the\n development of mobile disease diagnostics through machine\n learning and crowdsourcing},\n journal = {CoRR},\n volume = {abs/1511.08060},\n year = {2015},\n url = {http://arxiv.org/abs/1511.08060},\n archivePrefix = {arXiv},\n eprint = {1511.08060},\n timestamp = {Mon, 13 Aug 2018 16:48:21 +0200},\n biburl = {https://dblp.org/rec/bib/journals/corr/HughesS15},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}", @@ -125738,7 +125738,7 @@ 0.225, 0.223 ], - "examples_image_url": "/img/agml/sample_images/rangeland_weeds_australia_examples.png", + "examples_image_url": "/img/agml/sample_images/rangeland_weeds_australia_examples.webp", "source": "agml", "license": "CC BY-SA 4.0", "citation": "@Article{Olsen2019,\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 Calvert, Brendan and Azghadi, Mostafa Rahimi and White, Ronald D.},\n title={DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning},\n journal={Scientific Reports},\n year={2019},\n month={Feb},\n day={14},\n volume={9},\n number={1},\n pages={2058},\n abstract={Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1{\\%} and 95.7{\\%}, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands.},\n issn={2045-2322},\n doi={10.1038/s41598-018-38343-3},\n url={https://doi.org/10.1038/s41598-018-38343-3}\n}", @@ -125767,7 +125767,7 @@ 0.287, 0.283 ], - "examples_image_url": "/img/agml/sample_images/red_grapes_and_leaves_segmentation_examples.png", + "examples_image_url": "/img/agml/sample_images/red_grapes_and_leaves_segmentation_examples.webp", "source": "agml", "license": "", "citation": "@inproceedings{kalampokas2020semantic,\n title={Semantic segmentation of vineyard images using convolutional neural networks},\n author={Kalampokas, Theofanis and Tziridis, Konstantinos and Nikolaou, Alexandros and Vrochidou, Eleni and Papakostas, George A and Pachidis, Theodore and Kaburlasos, Vassilis G},\n booktitle={International Conference on Engineering Applications of Neural Networks},\n pages={292--303},\n year={2020},\n organization={Springer}}", @@ -125798,7 +125798,7 @@ 0.175, 0.135 ], - "examples_image_url": "/img/agml/sample_images/rice_leaf_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/rice_leaf_disease_classification_examples.webp", "source": "agml", "license": "", "citation": "", @@ -125829,7 +125829,7 @@ 0.108, 0.111 ], - "examples_image_url": "/img/agml/sample_images/rice_seedling_segmentation_examples.png", + "examples_image_url": "/img/agml/sample_images/rice_seedling_segmentation_examples.webp", "source": "agml", "license": "", "citation": "@Article{electronics9101602,\n AUTHOR = {Khan, Abbas and Ilyas, Talha and Umraiz, Muhammad and Mannan, Zubaer Ibna and Kim, Hyongsuk},\n TITLE = {CED-Net: Crops and Weeds Segmentation for Smart Farming Using a Small Cascaded Encoder-Decoder Architecture},\n JOURNAL = {Electronics},\n VOLUME = {9},\n YEAR = {2020},\n NUMBER = {10},\n ARTICLE-NUMBER = {1602},\n URL = {https://www.mdpi.com/2079-9292/9/10/1602},\n ISSN = {2079-9292},\n ABSTRACT = {Convolutional neural networks (CNNs) have achieved state-of-the-art performance in numerous aspects of human life and the agricultural sector is no exception. One of the main objectives of deep learning for smart farming is to identify the precise location of weeds and crops on farmland. In this paper, we propose a semantic segmentation method based on a cascaded encoder-decoder network, namely CED-Net, to differentiate weeds from crops. The existing architectures for weeds and crops segmentation are quite deep, with millions of parameters that require longer training time. To overcome such limitations, we propose an idea of training small networks in cascade to obtain coarse-to-fine predictions, which are then combined to produce the final results. Evaluation of the proposed network and comparison with other state-of-the-art networks are conducted using four publicly available datasets: rice seeding and weed dataset, BoniRob dataset, carrot crop vs. weed dataset, and a paddy–millet dataset. The experimental results and their comparisons proclaim that the proposed network outperforms state-of-the-art architectures, such as U-Net, SegNet, FCN-8s, and DeepLabv3, over intersection over union (IoU), F1-score, sensitivity, true detection rate, and average precision comparison metrics by utilizing only (1/5.74 × U-Net), (1/5.77 × SegNet), (1/3.04 × FCN-8s), and (1/3.24 × DeepLabv3) fractions of total parameters.},\n DOI = {10.3390/electronics9101602}\n}", @@ -125860,7 +125860,7 @@ 0.192, 0.177 ], - "examples_image_url": "/img/agml/sample_images/riseholme_strawberry_classification_2021_examples.png", + "examples_image_url": "/img/agml/sample_images/riseholme_strawberry_classification_2021_examples.webp", "source": "agml", "license": "", "citation": "@inproceedings{CWSC21,\n title={Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies},\n author={Choi, Taeyeong and Would, Owen and Salazar-Gomez, Adrian and Cielniak, Grzegorz},\n booktitle={2022 International Conference on Robotics and Automation (ICRA)},\n pages={2266--2272},\n year={2022},\n organization={IEEE}\n}", @@ -125891,7 +125891,7 @@ 0.23, 0.203 ], - "examples_image_url": "/img/agml/sample_images/soybean_insect_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/soybean_insect_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Mignoni, Maria Eloisa (2021), “Images of Soybean Leaves”, Mendeley Data, V1, doi: 10.17632/bycbh73438.1", @@ -125922,7 +125922,7 @@ 0.249, 0.157 ], - "examples_image_url": "/img/agml/sample_images/soybean_weed_uav_brazil_examples.png", + "examples_image_url": "/img/agml/sample_images/soybean_weed_uav_brazil_examples.webp", "source": "agml", "license": "CC BY-NC 3.0", "citation": "dos Santos Ferreira, Alessandro; Pistori, Hemerson; Matte Freitas, Daniel; Gonçalves da Silva, Gercina (2017), “Data for: Weed Detection in Soybean Crops Using ConvNets”, Mendeley Data, V2, doi: 10.17632/3fmjm7ncc6.2", @@ -125953,7 +125953,7 @@ 0.199, 0.205 ], - "examples_image_url": "/img/agml/sample_images/strawberry_detection_2022_examples.png", + "examples_image_url": "/img/agml/sample_images/strawberry_detection_2022_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "", @@ -125984,7 +125984,7 @@ 0.148, 0.148 ], - "examples_image_url": "/img/agml/sample_images/strawberry_detection_2023_examples.png", + "examples_image_url": "/img/agml/sample_images/strawberry_detection_2023_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "", @@ -126015,7 +126015,7 @@ 0.17, 0.17 ], - "examples_image_url": "/img/agml/sample_images/sugarbeet_weed_segmentation_examples.png", + "examples_image_url": "/img/agml/sample_images/sugarbeet_weed_segmentation_examples.webp", "source": "agml", "license": "GPL-3.0", "citation": "@ARTICLE{8115245,\n author={I. Sa and Z. Chen and M. Popović and R. Khanna and F. Liebisch and J. Nieto and R. Siegwart},\n journal={IEEE Robotics and Automation Letters},\n title={weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming},\n year={2018},\n volume={3},\n number={1},\n pages={588-595},\n keywords={agriculture;agrochemicals;autonomous aerial vehicles;control engineering computing;convolution;crops;feature extraction;image classification;learning (artificial intelligence);neural nets;vegetation;MAV;SegNet;convolutional neural network;crop health;crop management;curve classification metrics;dense semantic classes;dense semantic weed classification;encoder-decoder;input image channels;multispectral images;selective weed treatment;vegetation index;weed detection;Agriculture;Cameras;Image segmentation;Robots;Semantics;Training;Vegetation mapping;Aerial systems;agricultural automation;applications;robotics in agriculture and forestry},\n doi={10.1109/LRA.2017.2774979},\n ISSN={},\n month={Jan}\n}", @@ -126046,7 +126046,7 @@ 0.217, 0.192 ], - "examples_image_url": "/img/agml/sample_images/sugarcane_damage_usa_examples.png", + "examples_image_url": "/img/agml/sample_images/sugarcane_damage_usa_examples.webp", "source": "agml", "license": "", "citation": "@ARTICLE{8412587,\n author={Alencastre-Miranda, Moises and Davidson, Joseph R. and Johnson, Richard M. and Waguespack, Herman and Krebs, Hermano Igo},\n journal={IEEE Robotics and Automation Letters}, \n title={Robotics for Sugarcane Cultivation: Analysis of Billet Quality using Computer Vision}, \n year={2018},\n volume={3},\n number={4},\n pages={3828-3835},\n doi={10.1109/LRA.2018.2856999}}", @@ -126077,7 +126077,7 @@ 0.214, 0.18 ], - "examples_image_url": "/img/agml/sample_images/sunflower_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/sunflower_disease_classification_examples.webp", "source": "agml", "license": "CC BY 4.0", "citation": "Rajbongshi, Aditya; Sara, Umme ; Akter, Bonna ; Shakil, Rashiduzzaman ; Sazzad, Sadia (2022), “Sun Flower Fruits and Leaves dataset for Sunflower Disease Classification through Machine Learning and Deep Learning”, Mendeley Data, V1, doi: 10.17632/b83hmrzth8.1", @@ -126129,7 +126129,7 @@ 0.243, 0.325 ], - "examples_image_url": "/img/agml/sample_images/tea_leaf_disease_classification_examples.png", + "examples_image_url": "/img/agml/sample_images/tea_leaf_disease_classification_examples.webp", "source": "agml", "license": "CC BY-NC 4.0", "citation": "@article{BALASUNDARAM2025103784, title = {Tea leaf disease detection using segment anything model and deep convolutional neural networks}, journal = {Results in Engineering}, volume = {25}, pages = {103784}, year = {2025}, issn = {2590-1230}, doi = {https://doi.org/10.1016/j.rineng.2024.103784}, url = {https://www.sciencedirect.com/science/article/pii/S2590123024020279}, author = {Ananthakrishnan Balasundaram and Prem Sundaresan and Aryan Bhavsar and Mishti Mattu and Muthu Subash Kavitha and Ayesha Shaik}}", @@ -126160,7 +126160,7 @@ 0.148, 0.185 ], - "examples_image_url": "/img/agml/sample_images/tomato_leaf_disease_examples.png", + "examples_image_url": "/img/agml/sample_images/tomato_leaf_disease_examples.webp", "source": "agml", "license": "CC0: Public Domain", "citation": "", @@ -126191,7 +126191,7 @@ 0.193, 0.224 ], - "examples_image_url": "/img/agml/sample_images/tomato_ripeness_detection_examples.png", + "examples_image_url": "/img/agml/sample_images/tomato_ripeness_detection_examples.webp", "source": "agml", "license": "CC BY-NC-SA 4.0", "citation": "", @@ -126222,7 +126222,7 @@ 5.818, 4.19 ], - "examples_image_url": "/img/agml/sample_images/vegann_multicrop_presence_segmentation_examples.png", + "examples_image_url": "/img/agml/sample_images/vegann_multicrop_presence_segmentation_examples.webp", "source": "agml", "license": "CC BY-SA 4.0", "citation": " @article{Madec_Irfan_Velumani_Baret_David_Daubige_Samatan_Serouart_Smith_James_et al._2023, title={VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation}, volume={10}, ISSN={2052-4463}, url={https://www.nature.com/articles/s41597-023-02098-y}, DOI={10.1038/s41597-023-02098-y}, abstractNote={Abstract\n \n Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn -\n Veg\n etation\n Ann\n otation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research.}, number={1}, journal={Scientific Data}, author={Madec, Simon and Irfan, Kamran and Velumani, Kaaviya and Baret, Frederic and David, Etienne and Daubige, Gaetan and Samatan, Lucas Bernigaud and Serouart, Mario and Smith, Daniel and James, Chrisbin and Camacho, Fernando and Guo, Wei and De Solan, Benoit and Chapman, Scott C. and Weiss, Marie}, year={2023}, month=may, pages={302}, language={en} }\n", @@ -126251,7 +126251,7 @@ 0.228, 0.23 ], - "examples_image_url": "/img/agml/sample_images/vine_virus_photo_dataset_examples.png", + "examples_image_url": "/img/agml/sample_images/vine_virus_photo_dataset_examples.webp", "source": "agml", "license": "Apache 2.0", "citation": "", @@ -126282,7 +126282,7 @@ 0.217, 0.177 ], - "examples_image_url": "/img/agml/sample_images/wheat_head_counting_examples.png", + "examples_image_url": "/img/agml/sample_images/wheat_head_counting_examples.webp", "source": "agml", "license": "CC BY-SA 4.0", "citation": "@article{david2020global,\n title={Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods},\n author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul A and others},\n journal={Plant Phenomics},\n volume={2020},\n year={2020},\n publisher={Science Partner Journal}\n }", @@ -126313,7 +126313,7 @@ 0.246, 0.253 ], - "examples_image_url": "/img/agml/sample_images/white_grapes_and_leaves_segmentation_examples.png", + "examples_image_url": "/img/agml/sample_images/white_grapes_and_leaves_segmentation_examples.webp", "source": "agml", "license": "", "citation": "@inproceedings{kalampokas2020semantic,\n title={Semantic segmentation of vineyard images using convolutional neural networks},\n author={Kalampokas, Theofanis and Tziridis, Konstantinos and Nikolaou, Alexandros and Vrochidou, Eleni and Papakostas, George A and Pachidis, Theodore and Kaburlasos, Vassilis G},\n booktitle={International Conference on Engineering Applications of Neural Networks},\n pages={292--303},\n year={2020},\n organization={Springer}}", diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index d9daa1f..1b5a807 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": "/img/agml/sample_images/wGrapeUNIPD-DL_white_grape_bunch_detection_sample.png", + "examples_image_url": "/img/agml/sample_images/wGrapeUNIPD-DL_white_grape_bunch_detection_sample.webp", "license": "cc-by-4.0", "citation": null, "parent_dataset": null, @@ -53,7 +53,7 @@ ], "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/Strawberry-DS_strawberry_detection_sample.png", + "examples_image_url": "/img/agml/sample_images/Strawberry-DS_strawberry_detection_sample.webp", "license": null, "citation": null, "parent_dataset": null, @@ -81,7 +81,7 @@ ], "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/synthetic_cowpea_flower_detection_sample.png", + "examples_image_url": "/img/agml/sample_images/synthetic_cowpea_flower_detection_sample.webp", "license": null, "citation": null, "parent_dataset": null, @@ -110,7 +110,7 @@ ], "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/synthetic_cowpea_pod_detection_sample.png", + "examples_image_url": "/img/agml/sample_images/synthetic_cowpea_pod_detection_sample.webp", "license": null, "citation": null, "parent_dataset": null, @@ -139,7 +139,7 @@ ], "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/GEMINI_cowpea_pod_detection_sample.png", + "examples_image_url": "/img/agml/sample_images/GEMINI_cowpea_pod_detection_sample.webp", "license": null, "citation": null, "parent_dataset": null, @@ -167,7 +167,7 @@ ], "stats_mean": null, "stats_std": null, - "examples_image_url": "/img/agml/sample_images/GEMINI_cowpea_flower_detection_sample.png", + "examples_image_url": "/img/agml/sample_images/GEMINI_cowpea_flower_detection_sample.webp", "license": null, "citation": null, "parent_dataset": null, @@ -197,10 +197,10 @@ "Healthy Leaf", "Verticillium Wilt" ], - "examples_image_url": "/img/agml/sample_images/cotton_leaf_disease_classification_sample.png", + "examples_image_url": "/img/agml/sample_images/cotton_leaf_disease_classification_sample.webp", "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), \u201cCotton Leaf Image Dataset for Disease Classification \u201d, 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), “Cotton Leaf Image Dataset for Disease Classification ”, Mendeley Data, V1, doi: 10.17632/t9hgvk2h9p.1", "zip_size_bytes": 61300000, "augmented_zip_size_bytes": 359300000, "source": "huggingface", @@ -233,10 +233,10 @@ "Powdery Mildew", "Tobacco Mosaic Virus" ], - "examples_image_url": "/img/agml/sample_images/eggplant_leaf_disease_classification_sample.png", + "examples_image_url": "/img/agml/sample_images/eggplant_leaf_disease_classification_sample.webp", "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), \u201cEggplant Dataset: A Comprehensive Dataset for Agricultural Research and Disease Detection\u201d, Mendeley Data, V1, doi: 10.17632/5drkk544k8.1", + "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", "zip_size_bytes": 499400000, "augmented_zip_size_bytes": 767800000, "source": "huggingface", @@ -265,12 +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), \u201cMoringaLeafNet: A Multi-Class Leaf Disease Dataset for Precision Agriculture and Deep Learning Research\u201d, Mendeley Data, V5, doi: 10.17632/w8sr775pjb.5", + "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", "zip_size_bytes": 1130000000, "augmented_zip_size_bytes": 2860000000, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/MoringaLeafNet_disease_classification", - "examples_image_url": "/img/agml/sample_images/MoringaLeafNet_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/MoringaLeafNet_disease_classification_sample.webp" }, { "name": "ash_gourd_disease_classification", @@ -295,11 +295,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111997", - "citation": "Jahan, Nusrat; Hasan, Md Zahid (2024), \u201cAsh Gourd Leaf Healthy and Disease Dataset\u201d, Mendeley Data, V2, doi: 10.17632/zj4th6xvdp.2", + "citation": "Jahan, Nusrat; Hasan, Md Zahid (2024), “Ash Gourd Leaf Healthy and Disease Dataset”, 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", - "examples_image_url": "/img/agml/sample_images/ash_gourd_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/ash_gourd_disease_classification_sample.webp" }, { "name": "jujube_bruise_classification", @@ -322,12 +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), \u201cJujubeBruiseNet: A Dataset for Bruise Detection in Ziziphus mauritiana\u201d, Mendeley Data, V5, doi: 10.17632/3mtdhrwgfr.5", + "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", "zip_size_bytes": 21700000, "augmented_zip_size_bytes": 162900000, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/jujube_bruise_classification", - "examples_image_url": "/img/agml/sample_images/jujube_bruise_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/jujube_bruise_classification_sample.webp" }, { "name": "MangoImageBD_classification", @@ -363,12 +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), \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", + "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", "zip_size_bytes": 163600000, "augmented_zip_size_bytes": 1140000000, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/MangoImageBD_classification", - "examples_image_url": "/img/agml/sample_images/MangoImageBD_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/MangoImageBD_classification_sample.webp" }, { "name": "okra_maturity_classification", @@ -391,11 +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), \u201cOkra Image Dataset\u201d, Mendeley Data, V1, doi: 10.17632/jmhz4826f2.1", + "citation": "Bharathi, Nikhilesh Kumar; FJ, Ferbin ; Sivapatham, Shoba (2025), “Okra Image Dataset”, 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", - "examples_image_url": "/img/agml/sample_images/okra_maturity_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/okra_maturity_classification_sample.webp" }, { "name": "pomegranate_disease_classification", @@ -423,7 +423,7 @@ "zip_size_bytes": 778600000, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/pomegranate_disease_classification", - "examples_image_url": "/img/agml/sample_images/pomegranate_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/pomegranate_disease_classification_sample.webp" }, { "name": "rice_disease_classification_bangladesh", @@ -452,12 +452,12 @@ ], "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), \u201cRice Leaf Disease and Pest Dataset Overview\u201d, Mendeley Data, V1, doi: 10.17632/vwv3nry3wr.1", + "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", "zip_size_bytes": 550444671, "augmented_zip_size_bytes": 2828265966, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/rice_disease_classification_bangladesh", - "examples_image_url": "/img/agml/sample_images/rice_disease_classification_bangladesh_sample.png" + "examples_image_url": "/img/agml/sample_images/rice_disease_classification_bangladesh_sample.webp" }, { "name": "RoseLeafInsight_disease_classification", @@ -481,11 +481,11 @@ ], "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), \u201cRoseLeafInsight: A High-Resolution Image Dataset for Rose Leaf Disease Recognition\u201d, 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), “RoseLeafInsight: A High-Resolution Image Dataset for Rose Leaf Disease Recognition”, 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", - "examples_image_url": "/img/agml/sample_images/RoseLeafInsight_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/RoseLeafInsight_disease_classification_sample.webp" }, { "name": "taro_blight_stage_classification", @@ -512,11 +512,11 @@ ], "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), \u201cAn Image Dataset of Taro Leaf Blight Disease Collected from the West African Sub-Region\u201d, Mendeley Data, V2, doi: 10.17632/3knm93dkc5.2", + "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", "zip_size_bytes": 1275148970, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/taro_blight_stage_classification", - "examples_image_url": "/img/agml/sample_images/taro_blight_stage_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/taro_blight_stage_classification_sample.webp" }, { "name": "jute_disease_classification", @@ -545,7 +545,7 @@ "zip_size_bytes": 391487547, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/jute_disease_classification", - "examples_image_url": "/img/agml/sample_images/jute_disease_classification.png" + "examples_image_url": "/img/agml/sample_images/jute_disease_classification.webp" }, { "name": "grape_leaf_disease_classification", @@ -569,11 +569,11 @@ ], "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), \u201cNiphad Grape Leaf Disease Dataset (NGLD)\u201d, Mendeley Data, V5, doi: 10.17632/8nnd2ypcv3.5", + "citation": "Dharrao, Madhuri; Dharrao, Deepak; Sonawane, Rakesh; zade, Nilima (2025), “Niphad Grape Leaf Disease Dataset (NGLD)”, 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", - "examples_image_url": "/img/agml/sample_images/grape_leaf_disease_classification.png" + "examples_image_url": "/img/agml/sample_images/grape_leaf_disease_classification.webp" }, { "name": "durian_disease_classification_vietnam", @@ -599,11 +599,11 @@ ], "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), \u201cA Durian Leaf Image Dataset of Common Diseases in Vietnam for Agricultural Diagnosis \u201d, Mendeley Data, V4, doi: 10.17632/pxzvksbwnj.4", + "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", "zip_size_bytes": 183109270, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/durian_disease_classification_vietnam", - "examples_image_url": "/img/agml/sample_images/durian_disease_classification_vietnam_sample.png" + "examples_image_url": "/img/agml/sample_images/durian_disease_classification_vietnam_sample.webp" }, { "name": "teaLeafBD_disease_classification_classification", @@ -630,11 +630,11 @@ ], "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), \u201cteaLeafBD\u201d, 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), “teaLeafBD”, 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", - "examples_image_url": "/img/agml/sample_images/teaLeafBD_disease_classification_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/teaLeafBD_disease_classification_classification_sample.webp" }, { "name": "mango_growth_detection", @@ -658,11 +658,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111780", - "citation": "Kabir, Sayem ; Rashid, Mohammad Rifat Ahmmad (2024), \u201cImage Dataset for Mango Growth Stages Analysis\u201d, Mendeley Data, V1, doi: 10.17632/5snwpzdtzs.1", + "citation": "Kabir, Sayem ; Rashid, Mohammad Rifat Ahmmad (2024), “Image Dataset for Mango Growth Stages Analysis”, 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", - "examples_image_url": "/img/agml/sample_images/mango_growth_detection_sample.png" + "examples_image_url": "/img/agml/sample_images/mango_growth_detection_sample.webp" }, { "name": "betel_leaf_disease_classification_2", @@ -686,12 +686,12 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111564", - "citation": "Hridoy, Rashidul Hasan; Habib, Md Tarek; Mahmud, Imran; Haque, Aminul; Mamun, Md Abdulla Al (2025), \u201cComprehensive Betel Leaf Disease Dataset for Advanced Pathology Research \u201d, Mendeley Data, V1, doi: 10.17632/vpzkntzjty.1", + "citation": "Hridoy, Rashidul Hasan; Habib, Md Tarek; Mahmud, Imran; Haque, Aminul; Mamun, Md Abdulla Al (2025), “Comprehensive Betel Leaf Disease Dataset for Advanced Pathology Research ”, Mendeley Data, V1, doi: 10.17632/vpzkntzjty.1", "zip_size_bytes": 202790683, "augmented_zip_size_bytes": 1195037270, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/betel_leaf_disease_classification_2", - "examples_image_url": "/img/agml/sample_images/betel_leaf_disease_classification_2_sample.png" + "examples_image_url": "/img/agml/sample_images/betel_leaf_disease_classification_2_sample.webp" }, { "name": "coffee_bean_quality_classification", @@ -720,11 +720,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111434", - "citation": "Nair, Bipin (2024), \u201cCBD_Coffee Bean Dataset\u201d, Mendeley Data, V1, doi: 10.17632/52877z55vr.1", + "citation": "Nair, Bipin (2024), “CBD_Coffee Bean Dataset”, Mendeley Data, V1, doi: 10.17632/52877z55vr.1", "zip_size_bytes": 337175739, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/coffee_bean_quality_classification", - "examples_image_url": "/img/agml/sample_images/coffee_bean_quality_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/coffee_bean_quality_classification_sample.webp" }, { "name": "eggplant_disease_classification", @@ -751,12 +751,12 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111353", - "citation": "Howlader, Shakib; Ahamed, Md. Sabbir; Mojumdar, Mayen Uddin (2025), \u201cEggplant Leaf Disease Detection Dataset\u201d, Mendeley Data, V2, doi: 10.17632/d3ypkphghb.2", + "citation": "Howlader, Shakib; Ahamed, Md. Sabbir; Mojumdar, Mayen Uddin (2025), “Eggplant Leaf Disease Detection Dataset”, Mendeley Data, V2, doi: 10.17632/d3ypkphghb.2", "zip_size_bytes": 2711819609, "augmented_zip_size_bytes": 135488596, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/eggplant_disease_classification", - "examples_image_url": "/img/agml/sample_images/eggplant_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/eggplant_disease_classification_sample.webp" }, { "name": "olive_tree_crown_detection", @@ -775,11 +775,11 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111515", - "citation": "Hnida, Youness; Mahraz, Mohamed Adnane; Achebour, Ali; Yahyaouy, Ali; Riffi, Jamal; Tairi, Hamid (2024), \u201cOliveTreeCrownsDb: A High-Resolution UAV Dataset for Detection and Segmentation in Agricultural Computer Vision\u201d, Mendeley Data, V3, doi: 10.17632/xym8rd2srf.3", + "citation": "Hnida, Youness; Mahraz, Mohamed Adnane; Achebour, Ali; Yahyaouy, Ali; Riffi, Jamal; Tairi, Hamid (2024), “OliveTreeCrownsDb: A High-Resolution UAV Dataset for Detection and Segmentation in Agricultural Computer Vision”, Mendeley Data, V3, doi: 10.17632/xym8rd2srf.3", "zip_size_bytes": 2045083876, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/olive_tree_crown_detection", - "examples_image_url": "/img/agml/sample_images/olive_tree_crown_detection_sample.png" + "examples_image_url": "/img/agml/sample_images/olive_tree_crown_detection_sample.webp" }, { "name": "CS-D_tea_leaf_disease_classification", @@ -805,11 +805,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.112251", - "citation": "Gupta, Megha (2025), \u201cTea Leaf Dataset\u201d, Mendeley Data, V1, doi: 10.17632/94fzcdz8gz.1", + "citation": "Gupta, Megha (2025), “Tea Leaf Dataset”, Mendeley Data, V1, doi: 10.17632/94fzcdz8gz.1", "zip_size_bytes": 1989289395, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/CS-D_tea_leaf_disease_classification", - "examples_image_url": "/img/agml/sample_images/CS-D_tea_leaf_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/CS-D_tea_leaf_disease_classification_sample.webp" }, { "name": "turmeric_leaf_disease_classification", @@ -834,12 +834,12 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.112184", - "citation": "Hossain , Md Riyad ; Rashid, Mohammad Rifat Ahmmad; Jahangir , Tasfia binte ; Hossain , Md. Samir ; Rahman, Md. Mahamudur ; Gani , Raiyan ; Ahmed , Jubaer ; Islam, Raihan Ul; Khan, M. Saddam Hossain (2025), \u201cImage Dataset for Turmeric Plant Leaf Disease Detection \u201d, Mendeley Data, V2, doi: 10.17632/jtttfbx342.2", + "citation": "Hossain , Md Riyad ; Rashid, Mohammad Rifat Ahmmad; Jahangir , Tasfia binte ; Hossain , Md. Samir ; Rahman, Md. Mahamudur ; Gani , Raiyan ; Ahmed , Jubaer ; Islam, Raihan Ul; Khan, M. Saddam Hossain (2025), “Image Dataset for Turmeric Plant Leaf Disease Detection ”, Mendeley Data, V2, doi: 10.17632/jtttfbx342.2", "zip_size_bytes": 3313266143, "augmented_zip_size_bytes": 16019083, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/turmeric_leaf_disease_classification", - "examples_image_url": "/img/agml/sample_images/turmeric_leaf_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/turmeric_leaf_disease_classification_sample.webp" }, { "name": "banana_bunch_maturity_classification_classification", @@ -861,11 +861,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.112337", - "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", + "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", "zip_size_bytes": 4881666205, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/banana_bunch_maturity_classification_classification", - "examples_image_url": "/img/agml/sample_images/banana_bunch_maturity_classification_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/banana_bunch_maturity_classification_classification_sample.webp" }, { "name": "BrinjalFruitX_disease_classification", @@ -890,11 +890,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2026.112490", - "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", + "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", "zip_size_bytes": 1903658616, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/BrinjalFruitX_disease_classification", - "examples_image_url": "/img/agml/sample_images/BrinjalFruitX_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/BrinjalFruitX_disease_classification_sample.webp" }, { "name": "SIMPDV1_plant_classification", @@ -953,11 +953,11 @@ ], "license": "cc-by-nc-3.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111660", - "citation": "T, gopu; k, uma (2025), \u201cSIMPD V1: South Indian Medicinal Plants dataset (Version 1)\u201d, Mendeley Data, V2, doi: 10.17632/9d89vjcghv.2", + "citation": "T, gopu; k, uma (2025), “SIMPD V1: South Indian Medicinal Plants dataset (Version 1)”, Mendeley Data, V2, doi: 10.17632/9d89vjcghv.2", "zip_size_bytes": 5320816465, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/SIMPDV1_plant_classification", - "examples_image_url": "/img/agml/sample_images/SIMPDV1_plant_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/SIMPDV1_plant_classification_sample.webp" }, { "name": "TOM2024_disease_classification", @@ -1005,11 +1005,11 @@ ], "license": "cc-by-4.0", "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), \u201cTOM2024\u201d, 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), “TOM2024”, Mendeley Data, V1, doi: 10.17632/3d4yg89rtr.1", "zip_size_bytes": 132628646, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/TOM2024_disease_classification", - "examples_image_url": "/img/agml/sample_images/TOM2024_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/TOM2024_disease_classification_sample.webp" }, { "name": "vineyard_pruning_segmentation", @@ -1032,11 +1032,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111335", - "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", + "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", "zip_size_bytes": 3109602543, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/vineyard_pruning_segmentation", - "examples_image_url": "/img/agml/sample_images/vineyard_pruning_segmentation_sample.png" + "examples_image_url": "/img/agml/sample_images/vineyard_pruning_segmentation_sample.webp" }, { "name": "AFruitDB_fruit_grade_classification", @@ -1064,11 +1064,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111380", - "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", + "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", "zip_size_bytes": 3206870630, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/AFruitDB_fruit_grade_classification", - "examples_image_url": "/img/agml/sample_images/AFruitDB_fruit_grade_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/AFruitDB_fruit_grade_classification_sample.webp" }, { "name": "turmeric_disease_classification", @@ -1095,11 +1095,11 @@ ], "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), \u201cTurmeric Plant Disease Dataset: Advancing AI for Agricultural Sustainability\u201d, Mendeley Data, V2, doi: 10.17632/g46dvrcvwn.2", + "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", "zip_size_bytes": 261469058, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/turmeric_disease_classification", - "examples_image_url": "/img/agml/sample_images/turmeric_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/turmeric_disease_classification_sample.webp" }, { "name": "black_gram_disease_classification", @@ -1124,11 +1124,11 @@ ], "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), \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", + "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", "zip_size_bytes": 1961562620, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/black_gram_disease_classification", - "examples_image_url": "/img/agml/sample_images/black_gram_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/black_gram_disease_classification_sample.webp" }, { "name": "african_plum_grading_classification", @@ -1158,7 +1158,7 @@ "zip_size_bytes": 307720141, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/african_plum_grading_classification", - "examples_image_url": "/img/agml/sample_images/african_plum_grading_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/african_plum_grading_classification_sample.webp" }, { "name": "hog_plum_leaf_disease_classification", @@ -1182,11 +1182,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111311", - "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", + "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", "zip_size_bytes": 2747105764, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/hog_plum_leaf_disease_classification", - "examples_image_url": "/img/agml/sample_images/hog_plum_leaf_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/hog_plum_leaf_disease_classification_sample.webp" }, { "name": "dragonfruit_disease_classification", @@ -1212,11 +1212,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111411", - "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", + "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", "zip_size_bytes": 2288654839, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/dragonfruit_disease_classification", - "examples_image_url": "/img/agml/sample_images/dragonfruit_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/dragonfruit_disease_classification_sample.webp" }, { "name": "tea_leaf_disease_classification_bangladesh", @@ -1240,11 +1240,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111379", - "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", + "citation": "Ahmad, MD Hasan (2024), “Advanced Tea Crop Disease Study: High-Resolution Dataset for Precision Agriculture and Pathological Insight”, Mendeley Data, V4, doi: 10.17632/tt2smzrzrs.4", "zip_size_bytes": 258979082, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/tea_leaf_disease_classification_bangladesh", - "examples_image_url": "/img/agml/sample_images/tea_leaf_disease_classification_bangladesh_sample.png" + "examples_image_url": "/img/agml/sample_images/tea_leaf_disease_classification_bangladesh_sample.webp" }, { "name": "vegetable_classification_bangladesh_classification", @@ -1287,11 +1287,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111441", - "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", + "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", "zip_size_bytes": 2298701986, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/vegetable_classification_bangladesh", - "examples_image_url": "/img/agml/sample_images/vegetable_classification_bangladesh_sample.png" + "examples_image_url": "/img/agml/sample_images/vegetable_classification_bangladesh_sample.webp" }, { "name": "centella_asiatica_leaves", @@ -1314,11 +1314,11 @@ ], "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), \u201cCentella Asiatica Leaf Image Dataset\u201d, Mendeley Data, V2, doi: 10.17632/hrx2kgphy5.2", + "citation": "Suryawanshi, Yogesh; Wakode, Krishna; PATIL, Kailas; chumchu, prawit (2024), “Centella Asiatica Leaf Image Dataset”, Mendeley Data, V2, doi: 10.17632/hrx2kgphy5.2", "zip_size_bytes": 1640898283, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/centella_asiatica_leaves", - "examples_image_url": "/img/agml/sample_images/centella_asiatica_leaves_sample.png" + "examples_image_url": "/img/agml/sample_images/centella_asiatica_leaves_sample.webp" }, { "name": "date_palm_leaf_disease_classification", @@ -1347,11 +1347,11 @@ ], "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), \u201cDiseases of date palm leaves dataset\u201d, Mendeley Data, V2, doi: 10.17632/g684ghfxvg.2", + "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", "zip_size_bytes": 208299356, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/date_palm_leaf_disease_classification", - "examples_image_url": "/img/agml/sample_images/date_palm_leaf_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/date_palm_leaf_disease_classification_sample.webp" }, { "name": "rice_seedling_classification", @@ -1378,7 +1378,7 @@ "zip_size_bytes": 37940285, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/rice_seedling_classification", - "examples_image_url": "/img/agml/sample_images/rice_seedling_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/rice_seedling_classification_sample.webp" }, { "name": "bay_leaf_disease_classification", @@ -1401,11 +1401,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.111024", - "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", + "citation": "PAYGUDE, PRIYANKA; CHAVAN, PRASHANT; Gayakwad, Milind (2024), “Indian Bay Leaves or Cinnamomum Tamala Leaves Dataset”, Mendeley Data, V2, doi: 10.17632/s9t7sr52wg.2", "zip_size_bytes": 2844559582, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/bay_leaf_disease_classification", - "examples_image_url": "/img/agml/sample_images/bay_leaf_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/bay_leaf_disease_classification_sample.webp" }, { "name": "bean_cowpea_leaf_disease_classification", @@ -1432,11 +1432,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.111023", - "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", + "citation": "Rashid, Mohammad Rifat Ahmmad; Hasan, Mahamudul; Gani, Raiyan ; Kamara, Raka ; Tarin, Taslima Khan ; Rabbi, Sheikh Fajlay (2024), “Image Dataset for Bean and Cowpea Plant Leaf Disease Detection and Freshness Assessment from Bangladesh ”, Mendeley Data, V1, doi: 10.17632/ykvcrjffzd.1", "zip_size_bytes": 1254989904, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/bean_cowpea_leaf_disease_classification", - "examples_image_url": "/img/agml/sample_images/bean_cowpea_leaf_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/bean_cowpea_leaf_disease_classification_sample.webp" }, { "name": "papaya_leaf_disease_detection", @@ -1461,11 +1461,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.110910", - "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", + "citation": "Sarker, Arpita ; Mustofa, Sumaya; Ahad, Md Taimur (2024), “BDPapayaLeaf: A annotation based image dataset of papaya leaf disease.”, Mendeley Data, V2, doi: 10.17632/p997fvf526.2", "zip_size_bytes": 118718405, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/papaya_leaf_disease_detection", - "examples_image_url": "/img/agml/sample_images/papaya_leaf_disease_detection_sample.png" + "examples_image_url": "/img/agml/sample_images/papaya_leaf_disease_detection_sample.webp" }, { "name": "REMP_plant_classification", @@ -1516,11 +1516,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.110895", - "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", + "citation": "Islam, Mohammad Manzurul; Rahman, Sanjida; Hoque, Nahida; Mamun, Md. Al; Moheuddin, Md. Sultan (2024), “REMP: A Unique Dataset of Rare and Endangered Medicinal Plants in Bangladesh”, Mendeley Data, V1, doi: 10.17632/hnwrxg8zm8.1", "zip_size_bytes": 108863130, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/REMP_plant_classification", - "examples_image_url": "/img/agml/sample_images/REMP_plant_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/REMP_plant_classification_sample.webp" }, { "name": "cotton_leaf_disease_classification_bangladesh_2", @@ -1549,11 +1549,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.110913", - "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", + "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", "zip_size_bytes": 300324698, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/cotton_leaf_disease_classification_bangladesh_2", - "examples_image_url": "/img/agml/sample_images/cotton_leaf_disease_classification_bangladesh_2_sample.png" + "examples_image_url": "/img/agml/sample_images/cotton_leaf_disease_classification_bangladesh_2_sample.webp" }, { "name": "banana_guava_quality_classification", @@ -1577,11 +1577,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.111025", - "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", + "citation": "KUMARI, ABIBAN; Singh, Jaswinder (2024), “Fruits (Banana and Guava) datasets for non-destructive quality classifications”, Mendeley Data, V2, doi: 10.17632/56td5w4wz2.2", "zip_size_bytes": 477412221, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/banana_guava_quality_classification", - "examples_image_url": "/img/agml/sample_images/banana_guava_quality_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/banana_guava_quality_classification_sample.webp" }, { "name": "BDMANGO_variety_classification", @@ -1609,11 +1609,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.111241", - "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", + "citation": "Islam, Mohammad Manzurul; Das, Aritra; Hasan, Md. Rakibul; Rashid, Mohammad Rifat Ahmmad (2024), “Image Dataset of Bangladeshi Mango Leaf”, Mendeley Data, V5, doi: 10.17632/nnh69sng8p.5", "zip_size_bytes": 14898089, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/BDMANGO_variety_classification", - "examples_image_url": "/img/agml/sample_images/BDMANGO_variety_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/BDMANGO_variety_classification_sample.webp" }, { "name": "money_plant_disease_classification", @@ -1636,11 +1636,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.111216", - "citation": "Ahmad, MD Hasan (2024), \u201cAdvanced Dataset on Money Plant Diseases for AI Pathology Research\u201d, Mendeley Data, V3, doi: 10.17632/rzjww3vdxt.3", + "citation": "Ahmad, MD Hasan (2024), “Advanced Dataset on Money Plant Diseases for AI Pathology Research”, Mendeley Data, V3, doi: 10.17632/rzjww3vdxt.3", "zip_size_bytes": 10368132, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/money_plant_disease_classification", - "examples_image_url": "/img/agml/sample_images/money_plant_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/money_plant_disease_classification_sample.webp" }, { "name": "radish_leaf_disease_classification", @@ -1665,11 +1665,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.111263", - "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", + "citation": "Rashid, Mohammad Rifat Ahmmad; Hasan, Mahamudul; Gani, Raiyan ; Tarin, Taslima Khan ; Kamara, Raka ; Rabbi, Sheikh Fajlay (2024), “Image Dataset for Radish Plant Leaf Disease Detection and Freshness Assessment from Bangladesh ”, Mendeley Data, V1, doi: 10.17632/s973cz2jcd.1", "zip_size_bytes": 472584908, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/radish_leaf_disease_classification", - "examples_image_url": "/img/agml/sample_images/radish_leaf_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/radish_leaf_disease_classification_sample.webp" }, { "name": "IDDMSLD_spinach_leaf_disease_classification", @@ -1694,11 +1694,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2025.111293", - "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", + "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", "zip_size_bytes": 336744844, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/IDDMSLD_spinach_leaf_disease_classification", - "examples_image_url": "/img/agml/sample_images/IDDMSLD_spinach_leaf_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/IDDMSLD_spinach_leaf_disease_classification_sample.webp" }, { "name": "BananaImageBD_variety_classification", @@ -1724,11 +1724,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.111239", - "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", + "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), “BananaImageBD: A Comprehensive Image Dataset of Common Banana Varieties with Different Ripeness Stages in Bangladesh.”, Mendeley Data, V2, doi: 10.17632/ptfscwtnyz.2", "zip_size_bytes": 12210739, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/BananaImageBD_variety_classification", - "examples_image_url": "/img/agml/sample_images/BananaImageBD_variety_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/BananaImageBD_variety_classification_sample.webp" }, { "name": "BananaImageBD_ripeness_classification", @@ -1754,11 +1754,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.111239", - "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", + "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), “BananaImageBD: A Comprehensive Image Dataset of Common Banana Varieties with Different Ripeness Stages in Bangladesh.”, Mendeley Data, V2, doi: 10.17632/ptfscwtnyz.2", "zip_size_bytes": 3717349, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/BananaImageBD_ripeness_classification", - "examples_image_url": "/img/agml/sample_images/BananaImageBD_ripeness_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/BananaImageBD_ripeness_classification_sample.webp" }, { "name": "lemon_leaf_disease_classification", @@ -1789,11 +1789,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.111244", - "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", + "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", "zip_size_bytes": 356691214, "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/lemon_leaf_disease_classification", - "examples_image_url": "/img/agml/sample_images/lemon_leaf_disease_classification_sample.png" + "examples_image_url": "/img/agml/sample_images/lemon_leaf_disease_classification_sample.webp" }, { "name": "lentil_disease_classification", @@ -1817,11 +1817,11 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1016/j.dib.2024.111224", - "citation": "Mahamud, Eram ; Tapos, Md Assaduzzaman (2024), \u201cLentil Plant Disease Image Dataset (4 Class)\u201d, Mendeley Data, V2, doi: 10.17632/7vb77bz2st.2", + "citation": "Mahamud, Eram ; Tapos, Md Assaduzzaman (2024), “Lentil Plant Disease Image Dataset (4 Class)”, Mendeley Data, V2, doi: 10.17632/7vb77bz2st.2", "zip_size_bytes": 74050616, "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" + "examples_image_url": "/img/agml/sample_images/lentil_disease_classification_sample.webp" }, { "name": "GYMNSA_pear_rust_detection", @@ -1840,11 +1840,11 @@ "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", + "citation": "Maß, Virginia; Alirezazadeh, Pendar; Seidl-Schulz, Johannes; Leipnitz, Matthias; Fritzsche, Eric; Ibraheem, Rasheed Ali Adam; Geyer, Martin; Pflanz, Michael; Reim, Stefanie (2024), “GYMNSA dataset”, 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" + "examples_image_url": "/img/agml/sample_images/GYMNSA_pear_rust_detection_sample.webp" }, { "name": "oyster_mushroom_maturity_detection", @@ -1866,11 +1866,11 @@ ], "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", + "citation": "Duman, Sonay; Elewi, Abdullah; Hajhamed, Abdulsalam; Khankan, Rasheed ; Souag, Amina; Ahmed, Asma (2024), “Annotated Oyster Mushroom Images”, 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" + "examples_image_url": "/img/agml/sample_images/oyster_mushroom_maturity_detection_sample.webp" }, { "name": "guava_disease_classification", @@ -1899,11 +1899,11 @@ ], "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", + "citation": "Shihab, Md Montsir Rahman; Saim, Nafiu Islam; Mojumdar, Mayen Uddin (2024), “Image Dataset for Detection and classification of Diseases of Guava Fruits and Leaves ”, 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" + "examples_image_url": "/img/agml/sample_images/guava_disease_classification_sample.webp" }, { "name": "COLD_chili_leaf_disease_classification", @@ -1912,7 +1912,9 @@ "location": "India", "environment": "field", "real_or_synthetic": "real", - "crop_types": ["Chili"], + "crop_types": [ + "Chili" + ], "sensor_modality": "rgb", "input_data_format": "image_folder", "annotation_format": "classLabel", @@ -1932,7 +1934,7 @@ "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" + "examples_image_url": "/img/agml/sample_images/COLD_chili_leaf_disease_classification_sample.webp" }, { "name": "EfficientMaize_classification", @@ -1941,7 +1943,9 @@ "location": "Ghana", "environment": "lab", "real_or_synthetic": "real", - "crop_types": ["maize"], + "crop_types": [ + "maize" + ], "sensor_modality": "rgb", "input_data_format": "image_folder", "annotation_format": "classLabel", @@ -1958,7 +1962,7 @@ "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" + "examples_image_url": "/img/agml/sample_images/EfficientMaize_classification_sample.webp" }, { "name": "mulberry_leaf_variety_classification", @@ -1967,7 +1971,9 @@ "location": "Thailand", "environment": "Field", "real_or_synthetic": "real", - "crop_types": ["mulberry"], + "crop_types": [ + "mulberry" + ], "sensor_modality": "rgb", "input_data_format": "image_folder", "annotation_format": "classLabel", @@ -1990,7 +1996,7 @@ "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" + "examples_image_url": "/img/agml/sample_images/mulberry_leaf_variety_classification_sample.webp" }, { "name": "rice_grain_variety_classification", @@ -1999,7 +2005,9 @@ "location": "Bangladesh", "environment": "Lab", "real_or_synthetic": "real", - "crop_types": ["rice"], + "crop_types": [ + "rice" + ], "sensor_modality": "rgb", "input_data_format": "image_folder", "annotation_format": "classLabel", @@ -2034,7 +2042,7 @@ "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" + "examples_image_url": "/img/agml/sample_images/rice_grain_variety_classification_sample.webp" }, { "name": "COLD_onion_leaf_disease_classification", @@ -2043,7 +2051,9 @@ "location": "India", "environment": "field", "real_or_synthetic": "real", - "crop_types": ["Onion"], + "crop_types": [ + "Onion" + ], "sensor_modality": "rgb", "input_data_format": "image_folder", "annotation_format": "classLabel", @@ -2062,10 +2072,168 @@ "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" + "examples_image_url": "/img/agml/sample_images/COLD_onion_leaf_disease_classification_sample.webp" + }, + { + "name": "MedLeafX_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": "Bangladesh", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "Camphor", + "Haritaki", + "Sojina", + "Neem" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 10858, + "classes": [ + "Bacterial Spot", + "Healthy Leaf", + "Powdery Mildew", + "Shot Hole", + "Shot Hole Leaf", + "Yellow Leaf" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2025.111945", + "citation": "Ferdous, Md. Fahim; Nissan, Faysal Bin Khaled ; Nibir, Nur Muhammad ; Bijoy, Md Hasan Imam (2025), “AI-MedLeafX: A Large-Scale Computer Vision Dataset for Medicinal Plant Diagnosis”, Mendeley Data, V1, doi: 10.17632/zz7r5y4dc6.1", + "zip_size_bytes": 705955036, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/MedLeafX_disease_classification", + "examples_image_url": "/img/agml/sample_images/MedLeafX_disease_classification_sample.webp" + }, + { + "name": "cotton_weed_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": "United States", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cotton" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "boundingBox", + "num_images": 262, + "classes": [ + "weed", + "cotton" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2026.112483", + "citation": "Das, Anindita; Subburaj, Vinitha; Yang, Yong; Bednarz, Craig (2025), “A UAV Image Dataset for Object Detection with Annotations Generated Using LabelImg and Roboflow ”, Mendeley Data, V1, doi: 10.17632/sx2tphzvcw.1", + "zip_size_bytes": 2193177286, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/cotton_weed_detection", + "examples_image_url": "/img/agml/sample_images/cotton_weed_detection_sample.webp" + }, + { + "name": "Hibiscus_Tea_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": "Bangladesh", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "Hibiscus", + "Tea" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 1411, + "augmented_num_images": 12417, + "augmented_zip_size_bytes": 4348866831, + "classes": [ + "Tea Algal Spot", + "Tea Brown Blight", + "Citruspot", + "Early_Mild_Spotting", + "Fungal_Infected", + "Tea Grey Blight", + "Healthy", + "Mild_Edge_Damage", + "Tea Red Spot", + "Senescent", + "Slightly_Diseased", + "Wrinkled_Leaf" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2025.112357", + "citation": "Billah, Md Masum; Sagor, Saifuddin ; Shorif Uddin, Mohammad; Hossain, Shahariar (2025), “A Real-World Hibiscus and Tea Leaf Image Dataset for Classification”, Mendeley Data, V4, doi: 10.17632/5bzy89brkv.4", + "zip_size_bytes": 680126599, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/Hibiscus_Tea_disease_classification", + "examples_image_url": "/img/agml/sample_images/Hibiscus_Tea_disease_classification_sample.webp" + }, + { + "name": "soybean_variety_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "variety_classification", + "location": "Indonesia", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "soybean" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 12683, + "classes": [ + "Anjasmoro", + "Dega", + "Grobogan" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2026.112524", + "citation": "Syahraza, Muhammad Ariyo; Hanafiah, Diana Sofia ; Purnamasari, Fanindia; Nurhasanah, Rossy (2025), “Image Dataset of Local Indonesian Soybean Seed Varieties (Anjasmoro, Grobogan, and DEGA-1)”, Mendeley Data, V3, doi: 10.17632/c733bjz4m3.3", + "zip_size_bytes": 857721637, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/soybean_variety_classification", + "examples_image_url": "/img/agml/sample_images/soybean_variety_classification_sample.webp" + }, + { + "name": "three_plant_leaf_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": "Bangladesh", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "Aegle Marmelos", + "Hog Plum", + "Lemon" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 3941, + "augmented_num_images": 12295, + "augmented_zip_size_bytes": 59100493, + "classes": [ + "Anthracnose", + "Caterpillars_Infestation", + "Cercospora_Leaf", + "Citrus", + "Healthy_Leaf", + "Heathy_Leaf", + "Leaf_Curl", + "Leaf_Spot", + "Sooty_Mold" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2025.111590", + "citation": "Huq, Rezwan; Ahmed, Jubaer; Nessa, Maherun; Islam, Tasmia; Gani, Raiyan (2024), “Smartphone Image Dataset for Aegle Marmelos, Hog Plum and Lemon Plant Leaf ”, Mendeley Data, V1, doi: 10.17632/54r883j5zr.1", + "zip_size_bytes": 98465473, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/three_plant_leaf_disease_classification", + "examples_image_url": 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