Artificial_Seed_Chip Object Detection Dataset and Pre-Trained Model by Yanuar Bomantara

Images

12300 images

Explore Dataset

Here are a few use cases for this project:

Agriculture and Planting: The Artificial_Seed_Chip model can be used to help farmers and agricultural scientists identify the optimal I-Seed classes for different soil types and weather conditions, enabling them to achieve better crop yields and improve farm management practices.
Environment and Biodiversity: By identifying the different I-Seed classes, researchers and ecologists can study their prevalence in various ecosystems, monitor their impact on local biodiversity, and develop strategies to protect endangered seed species.
Concrete Quality Control: Since the example image shows a close-up of a concrete surface, the model could potentially be used to analyze the distribution of I-Seed classes within concrete mixes, aiding in quality control and the development of better-performing building materials.
Urban Planning and Landscape Design: The Artificial_Seed_Chip model can assist urban planners and landscape architects in selecting appropriate I-Seed classes for urban and suburban plantings, taking into account factors such as aesthetics, sustainability, and ecological compatibility.
Educational Resources: The model can be used as a learning tool for students and educators in fields related to botany, ecology, and environmental science, enabling them to better understand and differentiate between I-Seed classes and their respective characteristics.

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{
                            artificial_seed_chip_dataset,
                            title = { Artificial_Seed_Chip Dataset },
                            type = { Open Source Dataset },
                            author = { Yanuar Bomantara },
                            howpublished = { \url{ https://universe.roboflow.com/yanuar-bomantara/artificial_seed_chip } },
                            url = { https://universe.roboflow.com/yanuar-bomantara/artificial_seed_chip },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { mar },
                            note = { visited on 2024-04-20 },
                            }