---
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.}
    }"
---