AgroBot Object Detection Dataset and Pre-Trained Model by AgroBot

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Explore Dataset

Here are a few use cases for this project:

Precision Agriculture: Farmers and agricultural companies can use AgroBot to monitor their currant fields and accurately identify weeds for targeted treatment, promoting efficient use of resources and healthier crop growth.
Weed Control Research: Scientists studying weed control strategies can use this model to study the impact of various methods on different currant-weeds classes via an automated system that tracks weed incidence and recovery.
Smart Gardening Systems: Garden owners can implement the model in smart gardening systems to keep their currant plantations weed-free by identifying and removing weeds in real time, enabling better care and maintenance.
Pesticide Development: Agrochemical companies might use the AgroBot model to assess the efficiency of their products. By recognizing different currant-weeds classes, the model can provide valuable data on which weeds their pesticides are most effective against.
Agricultural Drones: The model can be incorporated into drone technology for aerial monitoring of large currant farms. The drones can automatically identify and map weed infestations for targeted interventions, allowing for broader and more efficient weed control.

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.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            agrobot-uhvlj_dataset,
                            title = { AgroBot Dataset },
                            type = { Open Source Dataset },
                            author = { AgroBot },
                            howpublished = { \url{ https://universe.roboflow.com/agrobot-v9vw2/agrobot-uhvlj } },
                            url = { https://universe.roboflow.com/agrobot-v9vw2/agrobot-uhvlj },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { apr },
                            note = { visited on 2024-04-29 },
                            }