Weed Detection Object Detection Dataset and Pre-Trained Model by Deep Learning Assignment

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Explore Dataset

Here are a few use cases for this project:

Crop Management Systems: The weed detection model can be integrated into crop management software to help farmers quickly identify different types of weeds in their fields. This would allow them to apply the proper treatment in a timely manner, improving crop yield and reducing losses due to weed infestations.
Smart Agriculture Drones: The model could be used in smart drones for precision farming. These drones can scan fields, detect and identify the types of weed present, and selectively spray herbicides to eliminate those weeds without affecting the crop plants.
Botanical Research: Researchers studying plant species and biodiversity could use the model as a tool to identify and catalog various weed species in different environments. This could contribute to studies of invasive species and their impact on local ecosystems.
Gardening Apps: The model could be incorporated into gardening apps to assist users in identifying and treating weeds in their gardens. This would be particularly useful for gardeners without extensive knowledge of different weed species.
Environment Monitoring: Environmental agencies can use this model to monitor the presence and spread of invasive weed in natural parks and similar settings. Early detection and identification can aid in the proper management of these invasive species and protect the biodiversity of these areas.

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{
                            weed-detection-d7dau_dataset,
                            title = { Weed Detection Dataset },
                            type = { Open Source Dataset },
                            author = { Deep Learning Assignment },
                            howpublished = { \url{ https://universe.roboflow.com/deep-learning-assignment-ewyc5/weed-detection-d7dau } },
                            url = { https://universe.roboflow.com/deep-learning-assignment-ewyc5/weed-detection-d7dau },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-04-26 },
                            }