| | --- |
| | license: apache-2.0 |
| | --- |
| | --- |
| | title: Coffee Fruits Segmentation Dataset |
| | version: 1.0 |
| | description: > |
| | This dataset consists of **1,593** images of coffee fruits, annotated for segmentation tasks. |
| | The dataset is designed to facilitate **computer vision research** and **machine learning applications** |
| | in **agriculture**, specifically in the classification and detection of coffee fruits at different |
| | ripeness stages. The annotations include **segmentation masks** for individual coffee fruits. |
| |
|
| | dataset: |
| | name: coffee_fruits |
| | media_type: image |
| | num_samples: 1593 |
| | persistent: true |
| | tags: [] |
| | |
| | schema: |
| | sample_fields: |
| | - name: id |
| | type: fiftyone.core.fields.ObjectIdField |
| | description: Unique identifier for each sample |
| | - name: filepath |
| | type: fiftyone.core.fields.StringField |
| | description: File path to the image sample |
| | - name: tags |
| | type: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) |
| | description: Optional list of tags associated with the sample |
| | - name: metadata |
| | type: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata) |
| | description: Metadata containing image properties (e.g., width, height, format) |
| | - name: created_at |
| | type: fiftyone.core.fields.DateTimeField |
| | description: Timestamp indicating when the sample was added to the dataset |
| | - name: last_modified_at |
| | type: fiftyone.core.fields.DateTimeField |
| | description: Timestamp of the last modification |
| | - name: detections |
| | type: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) |
| | description: Object detection annotations, if available |
| | - name: segmentations |
| | type: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) |
| | description: Instance or semantic segmentation annotations for coffee fruits |
| | |
| | annotations: |
| | segmentation: |
| | description: > |
| | Segmentation annotations for individual coffee fruits, enabling pixel-wise classification. |
| | format: COCO-style segmentation masks |
| | fields: |
| | - name: segmentations |
| | type: fiftyone.core.labels.Detections |
| | description: Segmentation mask annotations |
| | |
| | usage: |
| | - Ripeness Classification: Training models to identify different ripeness stages of coffee fruits |
| | - Yield Estimation: Analyzing fruit density for crop monitoring |
| | - Disease Detection: Identifying abnormal or diseased coffee fruits |
| | - Autonomous Harvesting: Assisting robotic systems in fruit identification and segmentation |
| |
|
| | loading_example: |
| | code: | |
| | import fiftyone as fo |
| | |
| | # Load the dataset |
| | dataset = fo.load_dataset("coffee_fruits") |
| | |
| | # Visualize in FiftyOne App |
| | session = fo.launch_app(dataset) |
| |
|
| | citations: |
| | - "@article{RAMOS20179, |
| | title = {Automatic fruit count on coffee branches using computer vision}, |
| | journal = {Computers and Electronics in Agriculture}, |
| | volume = {137}, |
| | pages = {9-22}, |
| | year = {2017}, |
| | issn = {0168-1699}, |
| | doi = {https://doi.org/10.1016/j.compag.2017.03.010}, |
| | url = {https://www.sciencedirect.com/science/article/pii/S016816991630922X}, |
| | author = {P.J. Ramos and F.A. Prieto and E.C. Montoya and C.E. Oliveros}, |
| | keywords = {Coffee, Linear model, Fruits on branches, Harvest}, |
| | abstract = {In this article, a non-destructive method is proposed to count the number of fruits on a coffee branch |
| | by using information from digital images of a single side of the branch and its growing fruits. |
| | The information obtained in this research will spawn a new generation of tools for coffee growers to use. |
| | It is an efficient, non-destructive, and low-cost method which offers useful information for them |
| | to plan agricultural work and obtain economic benefits from the correct administration of resources.} |
| | }" |
| | |
| | - "@article{RAMOS201883, |
| | title = {Measurement of the ripening rate on coffee branches by using 3D images in outdoor environments}, |
| | journal = {Computers in Industry}, |
| | volume = {99}, |
| | pages = {83-95}, |
| | year = {2018}, |
| | issn = {0166-3615}, |
| | doi = {https://doi.org/10.1016/j.compind.2018.03.024}, |
| | url = {https://www.sciencedirect.com/science/article/pii/S0166361517304931}, |
| | author = {Paula J. Ramos and Jonathan Avendaño and Flavio A. Prieto}, |
| | keywords = {Coffee, 3D analysis, Ripeness index, Harvest logistics}, |
| | abstract = {In this article, a method for determination of the ripening rate of coffee branches is presented. |
| | This is achieved through analysis of 3D information obtained with a monocular camera in outdoor environments |
| | and under uncontrolled lighting, contrast, and occlusion conditions. |
| | The study provides a maturation index, allowing correct determination of a branch as ready or not for harvest |
| | with 83% efficiency.} |
| | }" |
| | --- |
