Monochrome computer vision for detecting common external defects of mango
Krishna Kumar Patel1,2, A Kar1, M A Khan2
1Division of Food Science and Post Harvest Technology, IARI, New Delhi, 110 012 India.
Journal of Food Science and Technology
|October 11, 2021
Summary
A new computer vision system using monochrome cameras can effectively detect external defects in mangoes, improving quality control. This technology offers high accuracy and efficiency, crucial for India
Area of Science:
- Agricultural Engineering
- Computer Vision
- Food Quality Assurance
Background:
- India, the world's largest mango producer, faces challenges in global market share due to limitations in automated quality and safety assurance tools.
- External defects significantly reduce mangoes' market value and quality.
Purpose of the Study:
- To develop and evaluate a computer vision system for the rapid, non-destructive detection of external defects in mangoes.
- To assess the system's potential for improving mango quality and safety assurance.
Main Methods:
- Development of a computer vision algorithm utilizing monochrome cameras for defect detection.
- Performance evaluation based on accuracy, efficiency, and average inspection time.
Main Results:
- The developed algorithm achieved an average accuracy of 88.75% for defect detection.
- The system demonstrated high efficiency, with an average inspection efficiency of 97.88%.
- Successfully detected common external defects like black lesions and mechanical damage.
Conclusions:
- Monochrome computer vision systems are highly effective for detecting external mango defects.
- The developed system shows significant potential for enhancing automated sorting and grading processes in the mango industry.
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