pjramg/coffee_beans_extended

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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.} }"

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