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Updated: May 24, 2025
11:02
Fruit Volatile Analysis Using an Electronic Nose
Published on: March 30, 2012
21.2K
Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction.
Yuchi Li1, Zhigao Wang2, Aiwei Yang3
1School of Labor Economics, China University of Labor Relations, Beijing, 100048, China.
Scientific Reports
|March 2, 2025
Summary
This study introduces a novel approach to assess apple ripeness using text and image data. Optimized algorithms and enhanced YOLOv8 models significantly improve apple quality assessment for better postharvest management.
Area of Science:
- Agricultural Science
- Computer Vision
- Machine Learning
Background:
- Apple quality assessment is crucial for agricultural production.
- Apple ripeness is a key indicator of quality.
- Integrating diverse data sources can enhance assessment accuracy.
Purpose of the Study:
- To develop a robust framework for assessing apple ripeness.
- To leverage both structured (text) and unstructured (image) data.
- To improve decision-making in postharvest management.
Main Methods:
- Support Vector Regression (SVR) models optimized with Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) for text data.
- Enhanced-YOLOv8+ architecture with Detect Efficient Head (DEH) and Efficient Channel Attention (ECA) for image data.
- Synergistic application of text and image analysis for comprehensive ripeness prediction.
Main Results:
- WOA-optimized SVR showed superior generalization for text-based ripeness prediction.
- Enhanced-YOLOv8+ achieved precise apple localization and ripeness identification from images.
- Combined methods led to a significant enhancement in overall prediction accuracy.
Conclusions:
- The proposed approach offers a reliable method for apple quality and ripeness assessment.
- This study deepens the understanding of maturity indicators and their relation to observed data.
- Informed postharvest management decisions are facilitated by accurate ripeness evaluation.
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