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Related Experiment Video

Updated: Apr 19, 2026

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field
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Deep learning based instance segmentation of mandarin fruit slices for precision assessment and morphological

Anh Tuan Le1,2, Jinhyun Ahn3, Thanh Tuan Thai4,5,6

  • 1Faculty of Biology and Biotechnology, University of Science, Ho Chi Minh City, 700000, Vietnam.

Scientific Reports
|April 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an automated YOLOv8 framework for segmenting mandarin fruit slices, improving accuracy in quantifying traits for cultivar evaluation and quality assessment.

Keywords:
Instance segmentationMandarin fruitPhenotypingYOLOv8

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Image Analysis

Background:

  • Accurate quantification of mandarin fruit slice morphology and central core structure is vital for agricultural applications.
  • Manual measurements are labor-intensive and lack consistency, hindering research progress.

Purpose of the Study:

  • To develop an automated segmentation framework for mandarin fruit slices using YOLOv8.
  • To enable precise quantification of fruit segment morphology and central core structure.

Main Methods:

  • Utilized YOLOv8 for object detection and semantic segmentation of mandarin fruit slices.
  • Trained the model on a dataset of 280 cropped high-resolution images derived from 58 original cross-sectional images.
  • Validated performance using bounding box and semantic segmentation metrics.

Main Results:

  • YOLOv8 achieved near-perfect detection metrics (e.g., precision ~0.997, recall ~1.00).
  • Semantic segmentation demonstrated strong accuracy (e.g., precision = 0.997, recall ~1.00).
  • Model showed high accuracy in quantitative measurements with low mean absolute error, comparable to ground truth.

Conclusions:

  • The developed YOLOv8 framework provides a robust and highly accurate method for mandarin fruit slice phenotyping.
  • This automated approach significantly enhances efficiency and reproducibility in agricultural research, breeding programs, and quality evaluation.