сorn_disease_instance Object Detection Dataset and Pre-Trained Model by Ozat

Images

1376 images

Explore Dataset

Here are a few use cases for this project:

Agricultural Monitoring: This model can be integrated into Smart Farm monitoring systems. Drones equipped with cameras can scan crop fields, then the model can process each image, allowing the system to detect early signs of Northern Leaf Blight or Rust on corn plants, thus enabling farmers to apply treatments more promptly.
Plant Disease Identification App: Developers can use this model to develop a mobile application. Farmers or agricultural scientists can take a photo of the corn-leaf, and the app can analyze it and provide information about the potential disease, aiding in field exploration.
Educational Platform: The model can be used as an educational tool for teaching students or novice farmers about common corn diseases. They can learn to identify different types of diseases by comparing images processed through the model with those in textbooks or online resources.
Precision Farming: This model can assist in personalized treatment plans. By identifying the specific type of disease, farmers can apply specific treatments to infected plants instead of applying a broad spectrum treatment to all, saving resources and protecting healthier plants from unnecessary treatments.
Research Tool: Scientists studying the impact and propagation of Northern Leaf Blight and Rust can use this model to quickly analyze a vast number of images. They can track different strands, as well as evaluate and predict disease spread based on historical and current data.

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{ -orn_disease_instance_dataset,
    title = { сorn_disease_instance Dataset },
    type = { Open Source Dataset },
    author = { Ozat },
    howpublished = { \url{ https://universe.roboflow.com/ozat/-orn_disease_instance } },
    url = { https://universe.roboflow.com/ozat/-orn_disease_instance },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { sep },
    note = { visited on 2023-12-08 },
}