maize-fallarmyworm Instance Segmentation Dataset and Pre-Trained Model by mitsgwalior

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Explore Dataset

Here are a few use cases for this project:

Agricultural Pest Control: This model could be utilized by farmers or agribusinesses to automate the pest-detection process. By identifying various stages of the Fall Armyworm, they can employ effective measures in real-time to curtail its damaging effects, thereby improving crop quality and yield.
Research and Development: Agricultural scientists and researchers can use this model to understand the life cycle of Fall Armyworm and its effects on maize crops. This could assist in the development of more resistant maize varieties or more effective pest control methods.
Smart Farming Applications: Developers creating agriculture-related software or apps can incorporate this model to provide users with automatic pest recognition, increasing the value of their digital farming tools and supporting precision agriculture practices.
Drone Agriculture Surveillance: Drones equipped with cameras can use this model to scan large fields, identify affected crops, and map out infestation hotspots. This data could support precision spraying methods, directly targeting areas with Fall Armyworm presence.
Educational Purposes: This model could be used in academic settings or training programs for farmers, helping them learn to identify signs of Fall Armyworm damage. It can help improve their observational abilities and decision-making on pest 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.

YOLOv5

This project has a YOLOv5 model checkpoint available for inference with Roboflow Deploy. YOLOv5 is a proven and tested, production ready, 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{
                            maize-fallarmyworm_dataset,
                            title = { maize-fallarmyworm Dataset },
                            type = { Open Source Dataset },
                            author = { mitsgwalior },
                            howpublished = { \url{ https://universe.roboflow.com/mitsgwalior/maize-fallarmyworm } },
                            url = { https://universe.roboflow.com/mitsgwalior/maize-fallarmyworm },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jul },
                            note = { visited on 2024-05-03 },
                            }