Cotton plant disease prediction Object Detection Dataset and Pre-Trained Model by National College of Ireland

Images

1935 images

Explore Dataset

Here are a few use cases for this project:

Precision Agriculture: Farmers and agricultural specialists can use the Cotton Plant Disease Prediction model to analyze images of their cotton plants, identify diseases in real-time, and optimize the use of pesticides, fungicides, and other treatments to boost crop health and increase yields.
Smart Greenhouses: Integrate the model into smart greenhouse systems, enabling automated monitoring of cotton plants throughout their growth cycles. By identifying diseases and stress factors early, greenhouse managers can address issues promptly, improving plant health and overall productivity.
Agricultural Research: Scientists and researchers studying cotton diseases and agronomy can utilize this model to quickly analyze large volumes of images, identify trends, and test the effectiveness of various treatment methods.
Educational Resources: Educators in agricultural and botanical fields can use the Cotton Plant Disease Prediction model to create interactive learning materials, helping students better understand cotton diseases, their symptoms, and treatment options.
Plant Disease Early Warning Systems: By integrating the model with satellite or aerial images, governments and agricultural organizations can quickly assess the prevalence of diseases in large-scale cotton fields, providing early alerts to farmers and initiating targeted interventions to prevent the spread of diseases and minimize crop loss.

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{
                            cotton-plant-disease-prediction-igthk_dataset,
                            title = { Cotton plant disease prediction Dataset },
                            type = { Open Source Dataset },
                            author = { National College of Ireland },
                            howpublished = { \url{ https://universe.roboflow.com/national-college-of-ireland/cotton-plant-disease-prediction-igthk } },
                            url = { https://universe.roboflow.com/national-college-of-ireland/cotton-plant-disease-prediction-igthk },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-05-03 },
                            }