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Influence of Illumination on Color based Potato Defect Detection


Affiliations
1 Department of Computer Science and Engineering Punjabi University, Patiala 147 002, Punjab, India
2 Computer Centre, Punjabi University, Patiala 147 002, Punjab, India
 

The global vegetable market demands rigorous quality analysis of potato crops to define their reasonable market price. Highly accurate machine learning-based automated potato defect detection systems are necessary for this rising global market. On the other hand, artificial illuminating lamps used in these machine learning systems are decisive in determining their defect detection accuracy. Artificial illuminating lamps used in these machine learning systems should provide a perception of color that is as similar as possible to natural potato color. This paper analysed potato skin color measuring accuracy of seven different illumination lamps (L01 to L07). The analysis used ten potato samples with various defects, including one healthy potato. Experimental results proved that the lowest Δ𝐸∗was obtained when the Compact Fluorescent lamp 45W, 6500 Correlated Color Temperature (CCT) (L1) was used as an illuminating lamp. The proposed experimental study will help to develop machine learning methodologies in future research by generating higher accuracies for potato defect detection using an L01-type illuminating lamp.

Keywords

Computer vision, Food quality, Image acquisition, Measurement and instrumentation, Standardization.
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  • Influence of Illumination on Color based Potato Defect Detection

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Authors

Amrinder Singh Brar
Department of Computer Science and Engineering Punjabi University, Patiala 147 002, Punjab, India
Kawaljeet Singh
Computer Centre, Punjabi University, Patiala 147 002, Punjab, India

Abstract


The global vegetable market demands rigorous quality analysis of potato crops to define their reasonable market price. Highly accurate machine learning-based automated potato defect detection systems are necessary for this rising global market. On the other hand, artificial illuminating lamps used in these machine learning systems are decisive in determining their defect detection accuracy. Artificial illuminating lamps used in these machine learning systems should provide a perception of color that is as similar as possible to natural potato color. This paper analysed potato skin color measuring accuracy of seven different illumination lamps (L01 to L07). The analysis used ten potato samples with various defects, including one healthy potato. Experimental results proved that the lowest Δ𝐸∗was obtained when the Compact Fluorescent lamp 45W, 6500 Correlated Color Temperature (CCT) (L1) was used as an illuminating lamp. The proposed experimental study will help to develop machine learning methodologies in future research by generating higher accuracies for potato defect detection using an L01-type illuminating lamp.

Keywords


Computer vision, Food quality, Image acquisition, Measurement and instrumentation, Standardization.

References