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A Method for Sorting High-Quality Fresh Sichuan Pepper Based on a Multi-Domain Multi-Scale Feature Fusion Algorithm.

Pengjun Xiang1,2, Fei Pan1,2, Xuliang Duan1,2

  • 1College of Information Engineering, Sichuan Agricultural University, Ya'an 625014, China.

Foods (Basel, Switzerland)
|September 14, 2024
PubMed
Summary

This study introduces a novel MultiDomain YOLOv8 model for efficient post-harvest selection of Sichuan pepper. The advanced algorithm accurately segments and classifies pepper quality, improving processing efficiency and producer profits.

Keywords:
Sichuan pepper sortinginstance segmentationmachine visionmaturity classificationsmart agriculture

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Food Science

Background:

  • Post-harvest selection of high-quality Sichuan pepper is crucial for agricultural production.
  • Existing methods struggle with varying pepper postures and maturity levels.
  • Accurate visual analysis is needed for efficient sorting.

Purpose of the Study:

  • To develop an automated visual system for high-quality Sichuan pepper selection.
  • To improve the accuracy and efficiency of post-harvest processing.
  • To enhance producer profitability through better quality control.

Main Methods:

  • Proposed multi-scale frequency domain feature fusion module (MSF3M) and multi-scale dual-domain feature fusion module (MS-DFFM).
  • Developed a MultiDomain YOLOv8 network for segmentation and classification of Sichuan pepper.
  • Implemented a selection method based on average local pixel value difference.

Main Results:

  • MultiDomain YOLOv8-seg achieved 88.8% mAP50 for fresh Sichuan pepper segmentation (5.84 MB model size).
  • MultiDomain YOLOv8-cls reached 98.34% accuracy in Sichuan pepper maturity classification.
  • The MultiDomain YOLOv8 model demonstrated higher accuracy and a lighter structure than the baseline YOLOv8.

Conclusions:

  • The MultiDomain YOLOv8 model significantly enhances post-harvest processing efficiency for Sichuan pepper.
  • The developed system effectively reduces misjudgments in quality selection.
  • This technology offers substantial benefits for agricultural applications and producer profits.