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Related Experiment Video

Updated: Jun 13, 2026

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Published on: August 23, 2017

Dual-Guided Semi-Supervised Semantic Segmentation for Citrus Quality Evaluation.

Xufeng Xu1,2,3, Ruokai Guo1,2,3, Kai Guo1,2,3

  • 1College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.

Foods (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces UP-ETS, a novel semi-supervised model for citrus defect detection that reduces reliance on labeled data. The model improves segmentation accuracy, especially for challenging defects, offering practical value in precision agriculture.

Area of Science:

  • Computer Vision
  • Precision Agriculture
  • Machine Learning

Background:

  • Supervised semantic segmentation for citrus defect detection requires extensive labeled data, leading to high costs.
  • Existing semi-supervised methods struggle with pseudo-label instability and poor feature discriminability for complex defects.

Purpose of the Study:

  • To develop a dual-guided semi-supervised semantic segmentation model, UP-ETS, for efficient and accurate citrus surface defect detection.
  • To address challenges in noise propagation and feature learning for complex citrus defect segmentation.

Main Methods:

  • UP-ETS utilizes a Mean Teacher-Student framework incorporating Uncertainty Estimation (UE) with Kullback-Leibler divergence to stabilize pseudo-labels.
  • Prototype Contrastive Learning (PCL) is employed to enhance feature discriminability for complex citrus surface textures and blurred boundaries.
Keywords:
consistency regularizationdeep learningdefect segmentationpseudo-labelsemi-supervised learning

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Determination of Self-(In)compatibility and Inter-(In)compatibility Relationships in Citrus Using Manual Pollination, Microscopy, and S-Genotype Analyses
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Determination of Self-(In)compatibility and Inter-(In)compatibility Relationships in Citrus Using Manual Pollination, Microscopy, and S-Genotype Analyses

Published on: June 30, 2023

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Determination of Self-(In)compatibility and Inter-(In)compatibility Relationships in Citrus Using Manual Pollination, Microscopy, and S-Genotype Analyses
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Determination of Self-(In)compatibility and Inter-(In)compatibility Relationships in Citrus Using Manual Pollination, Microscopy, and S-Genotype Analyses

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Main Results:

  • UP-ETS achieved improved Dice scores (87.76% vs. 85.57% baseline) with only 1/16 labeled data.
  • The model demonstrated enhanced segmentation for difficult samples, including small targets and blurred regions.
  • UP-ETS outperformed other semi-supervised models in performance, parameters, and inference speed, showing robust cross-dataset generalization.

Conclusions:

  • The proposed UP-ETS model effectively mitigates the need for large annotated datasets in agricultural defect detection.
  • UE and PCL synergistically create a more structured and discriminative feature space, proving their efficacy.
  • UP-ETS offers significant practical value for agricultural deployment due to its efficiency and accuracy.