Crop monitoring Object Detection Dataset and Pre-Trained Model by Dagi

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Here are a few use cases for this project:

Precision Agriculture: Farmers can utilize the "Crop monitoring" computer vision model to monitor the health of their maize crops, enabling them to take timely actions to prevent the spread of infections, target specific areas for pesticide application, and optimize overall crop health.
Crop Disease Surveillance: Government agencies, agricultural inspectors, and NGOs can use this model to identify potential outbreaks of diseases affecting maize, enabling them to implement timely interventions and spread awareness among farmers to prevent extensive crop losses and ensure food security.
Insurance and Damage Assessment: Crop insurers can use the "Crop monitoring" model to assess the extent of damage to insured crops caused by diseases, helping them to determine appropriate insurance payout amounts, identify areas with high infection risks, and create targeted policies.
Smart Drones for Crop Management: Drones equipped with the "Crop monitoring" model can survey large areas of maize fields, quickly identifying infected plants and assessing crop health. This aerial perspective can be particularly useful in large-scale farming operations, where manual inspection is not feasible.
Maize Breeding Programs: Researchers and agricultural companies can use the "Crop monitoring" model to study the performance of different maize varieties and hybridizations in terms of disease resistance, facilitating the development of more resistant and robust maize strains for improved agricultural outcomes.

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{
                            crop-monitoring_dataset,
                            title = { Crop monitoring  Dataset },
                            type = { Open Source Dataset },
                            author = { Dagi },
                            howpublished = { \url{ https://universe.roboflow.com/dagi/crop-monitoring } },
                            url = { https://universe.roboflow.com/dagi/crop-monitoring },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jun },
                            note = { visited on 2024-05-09 },
                            }