PlantDoc Dataset

# Overview

The PlantDoc dataset was originally published by researchers at the Indian Institute of Technology, and described in depth in [their paper](https://arxiv.org/pdf/1911.10317.pdf). One of the paper’s authors, Pratik Kayal, shared the object detection dataset available [on GitHub](https://github.com/pratikkayal/PlantDoc-Dataset).

PlantDoc is a dataset of 2,569 images across 13 plant species and 30 classes (diseased and healthy) for image classification and object detection. There are 8,851 labels. Read more about how the version available on Roboflow improves on the original version [here](https://blog.roboflow.ai/introducing-an-improved-plantdoc-dataset-for-plant-disease-object-detection/).

And here's an example image:


`Fork` this dataset (upper right hand corner) to receive the raw images, or (to save space) grab the 416x416 export.

# Use Cases

As the researchers from IIT stated in their paper, “plant diseases alone cost the global economy around US$220 billion annually.” Training models to recognize plant diseases earlier dramatically increases yield potential.

The dataset also serves as a useful open dataset for benchmarks. The researchers trained both object detection models like MobileNet and Faster-RCNN and image classification models like VGG16, InceptionV3, and InceptionResnet V2.

The dataset is useful for advancing general agriculture computer vision tasks, whether that be health crop classification, plant disease classification, or plant disease objection.

# Using this Dataset

This dataset follows [Creative Commons 4.0 protocol](https://creativecommons.org/licenses/by/4.0/). You may use it commercially without Liability, Trademark use, Patent use, or Warranty.

Provide the following citation for the original authors:

```
@misc{singh2019plantdoc,
title={PlantDoc: A Dataset for Visual Plant Disease Detection},
author={Davinder Singh and Naman Jain and Pranjali Jain and Pratik Kayal and Sudhakar Kumawat and Nipun Batra},
year={2019},
eprint={1911.10317},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```

# About Roboflow

[Roboflow](https://roboflow.ai) makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

#### [](https://roboflow.ai)

{'# Overview': '概述', 'The PlantDoc dataset was originally published by researchers at the Indian Institute of Technology, and described in depth in [their paper](https://arxiv.org/pdf/1911.10317.pdf). One of the paper’s authors, Pratik Kayal, shared the object detection dataset available [on GitHub](https://github.com/pratikkayal/PlantDoc-Dataset).': 'PlantDoc数据集最初由印度理工学院的研究人员发布，并在其论文中进行了深入描述。[论文链接](https://arxiv.org/pdf/1911.10317.pdf)。该论文作者之一的Pratik Kayal分享了可在GitHub上获取的对象检测数据集。[GitHub链接](https://github.com/pratikkayal/PlantDoc-Dataset)。', 'PlantDoc is a dataset of 2,569 images across 13 plant species and 30 classes (diseased and healthy) for image classification and object detection. There are 8,851 labels. Read more about how the version available on Roboflow improves on the original version [here](https://blog.roboflow.ai/introducing-an-improved-plantdoc-dataset-for-plant-disease-object-detection/).': 'PlantDoc数据集包含2,569张图像，涵盖13种植物物种和30个类别（病害和健康），用于图像分类和对象检测。共有8,851个标签。更多关于Roboflow上可用的版本如何改进原始版本的信息，请参阅[此处](https://blog.roboflow.ai/introducing-an-improved-plantdoc-dataset-for-plant-disease-object-detection/)。', "And here's an example image: ": '以下是一张示例图像：', 'Fork this dataset (upper right hand corner) to receive the raw images, or (to save space) grab the 416x416 export.': '通过在右上角点击“Fork”来获取原始图像，或者（为了节省空间）获取416x416的导出版本。', '# Use Cases': '应用场景', 'As the researchers from IIT stated in their paper, “plant diseases alone cost the global economy around US$220 billion annually.” Training models to recognize plant diseases earlier dramatically increases yield potential.': '正如印度理工学院的研究人员在论文中所指出的，“仅植物病害就使全球经济每年损失约2200亿美元。”训练模型以提前识别植物病害，可以显著提高产量潜力。', 'The dataset also serves as a useful open dataset for benchmarks. The researchers trained both object detection models like MobileNet and Faster-RCNN and image classification models like VGG16, InceptionV3, and InceptionResnet V2.': '该数据集还作为有用的公开基准数据集。研究人员训练了包括MobileNet和Faster-RCNN在内的对象检测模型，以及VGG16、InceptionV3和InceptionResnet V2在内的图像分类模型。', 'The dataset is useful for advancing general agriculture computer vision tasks, whether that be health crop classification, plant disease classification, or plant disease objection.': '该数据集对于推进农业计算机视觉任务具有重要意义，无论是健康作物分类、植物病害分类还是植物病害对象检测。', '# Using this Dataset': '使用此数据集', 'This dataset follows [Creative Commons 4.0 protocol](https://creativecommons.org/licenses/by/4.0/). You may use it commercially without Liability, Trademark use, Patent use, or Warranty.': '该数据集遵循[Creative Commons 4.0协议](https://creativecommons.org/licenses/by/4.0/)。您可以在商业用途中自由使用，无需承担法律责任、商标使用、专利使用或保修责任。', 'Provide the following citation for the original authors: @misc{singh2019plantdoc, title={PlantDoc: A Dataset for Visual Plant Disease Detection}, author={Davinder Singh and Naman Jain and Pranjali Jain and Pratik Kayal and Sudhakar Kumawat and Nipun Batra}, year={2019}, eprint={1911.10317}, archivePrefix={arXiv}, primaryClass={cs.CV}}': '为原作者提供以下引用：@misc{singh2019plantdoc, title={PlantDoc: A Dataset for Visual Plant Disease Detection}, author={Davinder Singh and Naman Jain and Pranjali Jain and Pratik Kayal and Sudhakar Kumawat and Nipun Batra}, year={2019}, eprint={1911.10317}, archivePrefix={arXiv}, primaryClass={cs.CV}}', '# About Roboflow': '关于Roboflow', '[Roboflow](https://roboflow.ai) makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.': '[Roboflow](https://roboflow.ai)使计算机视觉数据集的管理、预处理、增强和版本控制变得无缝。', "Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.": '开发者在使用Roboflow的工作流程时可以减少50%的代码量，自动化标注质量保证，节省训练时间，并提高模型的可复现性。', '#### [](https://roboflow.ai)': '#### [](https://roboflow.ai)'}