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.disease-detectionDurian-Plantation-Disease-Leaf-Rot-Detection-Image-Dataset
Durian Plantation Disease Leaf Rot Detection Image Dataset
Ensures high-quality data through multiple rounds of annotation and automated consistency checks, combined with reviews by agricultural pathology experts. The annotation team consists of 10 professionals in agriculture and computer vision. Pre-processing steps include noise reduction, size adjustments, and color normalization to enhance the model's recognition capabilities. Data is stored in JPG format, organized… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Durian-Plantation-Disease-Leaf-Rot-Detection-Image-Dataset.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.cabbage-disease-detection-v2
Cabbage Disease Detection Dataset V2
Description
This is a supplementary or variant dataset focusing on cabbage disease detection, containing labeled imagery for agricultural analysis.
Usage
from datasets import load_dataset
dataset = load_dataset("Arko007/cabbage-disease-detection-v2")
Agricultural-Pest-Disease-Image-Annotation-Bounding-Box-Polygon-Detection
Agricultural Pest & Disease Image Annotation (Bounding Box/Polygon Detection)
For intelligent pest and disease recognition in the agriculture and livestock industry, this dataset provides target region annotations on raw plant/disease images. Annotation results support both bounding boxes and polygon shapes, enabling models to learn object locations and fine-grained contours. It covers common pest/disease targets and supplies strong supervision for detection and segmentation… See the full description on the dataset page: https://huggingface.co/datasets/shangzx/Agricultural-Pest-Disease-Image-Annotation-Bounding-Box-Polygon-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.poultry-disease-detection-audio-datasetyolo-tea-leaf-pest-disease-detection
茶叶病虫害检测数据集
本数据集为茶叶病虫害检测数据集，适用于智慧农业领域的目标检测任务。
数据集标签信息
检测类别数（nc）：7
类别名称：Black Rot（黑腐病）, Brown Blight（褐枯病）, Healthy（健康）, Leaf Rust（叶锈病）, Mosquito Bug（蚊虫害）, Red Spider Mite（红蜘蛛螨）, White Scab
适用场景
Black Rot, Brown Blight, Healthy等多类目标的智能检测
农业智能化检测与病虫害识别
目标检测模型训练与部署
计算机视觉算法研究
网盘地址
在线下载链接：https://www.data2.cn
在线获取数据集：茶叶病虫害检测数据集
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yolo-asphalt-and-cement-pavement-disease-detection
沥青与水泥路面病害检测
本数据集为沥青与水泥路面病害检测，适用于智慧交通领域的目标检测任务。
数据集标签信息
检测类别数（nc）：1
类别名称：crack（裂纹）
适用场景
crack的智能识别与检测
基础设施智能巡检与病害检测
目标检测模型训练与部署
计算机视觉算法研究
网盘地址
在线下载链接：https://www.data2.cn
在线获取数据集：沥青与水泥路面病害检测
访问 https://www.data2.cn 访问 data2.cn 获取下载链接
plant-disease-detectionplant-disease-detection-hyperspectral-imagingDurian-Plantation-Disease-Leaf-Rot-Detection-Image-Dataset
Durian Plantation Disease Leaf Rot Detection Image Dataset
Ensures high-quality data through multiple rounds of annotation and automated consistency checks, combined with reviews by agricultural pathology experts. The annotation team consists of 10 professionals in agriculture and computer vision. Pre-processing steps include noise reduction, size adjustments, and color normalization to enhance the model's recognition capabilities. Data is stored in JPG format, organized… See the full description on the dataset page: https://huggingface.co/datasets/shangzx/Durian-Plantation-Disease-Leaf-Rot-Detection-Image-Dataset.cocoa-disease-detectionyolo-roof-disease-detection
屋顶病害检测数据集
本数据集为屋顶病害检测数据集，适用于计算机视觉领域的实例分割任务。
数据集标签信息
类别名称：Air Conditioner（空调外机）, Chimney（烟囱）, Fan（风扇）, Roof（屋顶）, Satellite Dish（卫星天线）, Skylight（天窗）, Solar Panel（太阳能板）, Vent, WindowRoof
适用场景
Air Conditioner, Chimney, Fan等多类目标的智能检测
实例分割模型训练与部署
计算机视觉算法研究
网盘地址
在线下载链接：https://www.data2.cn
在线获取数据集：
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eye-disease-detectionplant_disease_detectionokra-disease-detectiondisease_detectionEye-Disease-DetectionHeart_Disease_DetectionPlant-Disease-Detection-SystemAlzheimer-Disease-Detectionplant-disease-detection