datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
plant_disease_detection_processedThis Dataset is created from processing the files from this GitHub repository : PlantDoc-Object-Detection-Dataset
Citation
BibTeX:
@inproceedings{10.1145/3371158.3371196,
author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},
title = {PlantDoc: A Dataset for Visual Plant Disease Detection},
year = {2020},
isbn = {9781450377386},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url =… See the full description on the dataset page: https://huggingface.co/datasets/susnato/plant_disease_detection_processed.unbaised_skin_disease_detectiontomato_disease_detection
Tomato Disease Detection/Classification
A dataset containing 1026 images of diseased tomato plants. There are 417 images of Tomato Viral, 82 images of Gray Mold, and 527 images of Bacterial Wilt.Bounding boxes for the images represent the location of disease in the image.There are 10 classes:Gray Mold contains GrayMold_Fruit (0) and GrayMold_Leaf (1)Viral contains Viral_Leaf (2), Viral_Top (3), and Virus_Middle (4)Wilt contains Wilt_Base (5), Wilt_Leaf (6), Wilt_Middle (7)… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/tomato_disease_detection.tomato_leaf_disease_detection
Tomato Leaf Disease Detection
This dataset provides real-world RGB images of tomato leaves affected by various diseases, captured in agricultural field environments across multiple districts in Bangladesh. Images were collected using Canon EOS M50 cameras under natural outdoor conditions to support object detection tasks for disease identification in real farming scenarios. The dataset contains 689 images with 2,278 bounding box annotations across 7 categories.
This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/tomato_leaf_disease_detection.leaf_disease_detection
Leaf Disease Detection Dataset
Dataset Description
This dataset is designed for few-shot learning in the context of plant disease detection, specifically targeting tea anthracnose, tea brown blight, cotton fusarium wilt, and cotton powdery mildew. The dataset is a combination of self-collected data from tea gardens in Ya'an City, Sichuan Province, China, and publicly available disease image resources from the internet. It aims to support the development and evaluation of… See the full description on the dataset page: https://huggingface.co/datasets/ttkqwe123/leaf_disease_detection.Disease_DetectionRoCoLe_disease_detection
RoCoLe Disease Detection
A dataset for detection of Robusta coffee leaf diseases. The dataset contains 1,560 images with 1,560 bounding box annotations across 6 categories, as well as segmentation masks.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{parraga2019rocole,
title={RoCoLe: A robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/RoCoLe_disease_detection.papaya_leaf_disease_detection
Papaya Leaf Disease Detection
A dataset for disease detection of Papaya leaves. The dataset contains 1,050 images with 7,616 bounding box annotations across 5 categories.The dataset can be used as a classification dataset based on the label column, which contains integer based labels for the following classes:Anthracnose: 0Bacterial Spot: 1Curl: 2Ring Spot: 3Healthy: 4
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/papaya_leaf_disease_detection.plant_disease_detection_processedThis Dataset is created from processing the files from this GitHub repository : PlantDoc-Object-Detection-Dataset
Citation
BibTeX:
@inproceedings{10.1145/3371158.3371196,
author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},
title = {PlantDoc: A Dataset for Visual Plant Disease Detection},
year = {2020},
isbn = {9781450377386},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url =… See the full description on the dataset page: https://huggingface.co/datasets/gokulrsa/plant_disease_detection_processed.plant-disease-detectioncocoa-disease-detection