pineapple Object Detection Dataset and Pre-Trained Model by yolov5

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Explore Dataset

Here are a few use cases for this project:

Agriculture Evaluation: The model can be used by farmers or agricultural companies to assess the health and ripeness of pineapples in the field, helping to optimize harvest time and increase yield.
Quality Control in Food Industries: Manufacturers can employ the model to conduct quality control inspection in real-time on the production line, checking the quality, size, and maturity of pineapples to maintain consistent product standards.
Grocery Retail Assistance: Grocery stores can implement this model to automate the process of inventory assessment, detecting overripe or damaged pinapples, and thus helping with restocking and waste reduction.
Augmented Reality Education: The model can be integrated into an AR application used for educational purposes, helping students to learn about different parts of a pineapple or different types of pineapples through interactive visualization.
Biometric Authentication for Greenhouses: Using this model, access to specific areas containing pineapples within a smart greenhouse could be limited to only the staff members responsible for them – the camera would scan for the presence of a pineapple as part of the biometric checking process. This would ensure that only authorized personnel are able to interact with 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.

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{
                            pineapple-xgyxq_dataset,
                            title = { pineapple Dataset },
                            type = { Open Source Dataset },
                            author = { yolov5 },
                            howpublished = { \url{ https://universe.roboflow.com/yolov5-dxhko/pineapple-xgyxq } },
                            url = { https://universe.roboflow.com/yolov5-dxhko/pineapple-xgyxq },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-06-15 },
                            }