Detecting diseases Computer Vision Project
Beans, Strawberry and Tomato diseases - v1 2022-09-01
This dataset was exported via roboflow.com on Sep 2, 2022
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It includes 5494 images. Diseases are annotated in YOLO v7 PyTorch format.
The following pre-processing was applied to each image:
- Auto-orientation of pixel data (with EXIF-orientation stripping)
- Resize to 416x416 (Stretch)
No image augmentation techniques were applied.
Classes: Strawberry 'Angular Leafspot' 'Anthracnose Fruit Rot' 'Blossom Blight' 'Gray Mold' 'Leaf Spot' 'Powdery Mildew Fruit' 'Powdery Mildew Leaf' Tomato 'disease' 'leaf mold' 'spider mites' Bean 'ALS' 'Bean Rust'
Trained Model API
This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.
Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
detecting-diseases_dataset,
title = { Detecting diseases Dataset },
type = { Open Source Dataset },
author = { Artificial Intelligence },
howpublished = { \url{ https://universe.roboflow.com/artificial-intelligence-82oex/detecting-diseases } },
url = { https://universe.roboflow.com/artificial-intelligence-82oex/detecting-diseases },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2024-04-25 },
}
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Source
Last Updated
a year ago
Project Type
Object Detection
Subject
Disease
Views: 11653
Downloads: 550
