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Double yolk eggs detection using fuzzy logic.
Thanasan Intarakumthornchai1, Ramil Kesvarakul2
1Department of Industrial Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.
Plos One
|November 5, 2020
Summary
Detecting double-yolk (DY) chicken eggs using fuzzy logic based on weight and egg size ratio significantly reduces misclassification errors. This method enables the development of a cost-effective prototype for automated egg sorting in the growing egg industry.
Area of Science:
- Agricultural Engineering
- Artificial Intelligence in Agriculture
- Food Science and Technology
Background:
- Global increase in chicken egg production necessitates efficient sorting methods.
- Double-yolk (DY) eggs command higher prices, creating economic incentive for separation.
- Current manual sorting of DY eggs is labor-intensive and error-prone.
Purpose of the Study:
- To develop a low-cost prototype for detecting double-yolked (DY) chicken eggs.
- To improve the accuracy of DY egg detection using fuzzy logic.
- To reduce the cost associated with skilled labor for egg sorting.
Main Methods:
- Utilized fuzzy logic algorithms incorporating egg weight and size ratio for DY egg detection.
- Compared fuzzy logic with K-means clustering, noting K-means' higher error rate (15.05%).
- Developed a prototype using an Mbed platform, load cell, and OpenMV CAM for real-time measurement.
Main Results:
- Fuzzy logic achieved a low error rate of 1.43% in misclassifying single-yolk (SY) eggs as DY.
- The accuracy of fuzzy logic detection is dependent on the input membership functions (MFs).
- Triangular MFs for weight (low, medium, high) and ratio (low, medium, high) were selected.
Conclusions:
- Fuzzy logic offers a promising, low-error approach for automated DY egg detection.
- The developed prototype demonstrates the feasibility of cost-effective automated egg sorting.
- This technology can enhance revenue for egg producers and traders by improving sorting efficiency.
