Pea plant quality assesment Object Detection Dataset and Pre-Trained Model by kamalkheil

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Explore Dataset

Here are a few use cases for this project:

Agricultural Disease Identification: This model could be used by farmers or agricultural office to swiftly identify the type of disease a pea plant might be suffering from. This will aid in making well-informed decisions on the required treatments.
Plant Care App: The model could serve as a backbone of a mobile application aimed at garden enthusiasts. Users can take pictures of their pea plants, and the app would be able to identify leaf type and possible disease, giving advice on plant care.
Educational Tool: The model can be used in educational settings for botany or plant pathology courses, training students to identify different types of pea plant leaves and associated diseases.
Seed Producers Quality Assessment: Seed producers can use this model to categorize their produce, helping to guarantee the quality of the seeds being sold to farmers based on images of the plants they produce.
Research Studies: Researchers working on plant diseases might utilize this model to analyze and study the effect of different environmental conditions on pea plants, by monitoring leaves and pods' health.

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{
                            pea-plant-quality-assesment_dataset,
                            title = { Pea plant quality assesment Dataset },
                            type = { Open Source Dataset },
                            author = { kamalkheil },
                            howpublished = { \url{ https://universe.roboflow.com/kamalkheil/pea-plant-quality-assesment } },
                            url = { https://universe.roboflow.com/kamalkheil/pea-plant-quality-assesment },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-05-11 },
                            }