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Color quantization and image analysis for automated fruit quality evaluation

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4 Author(s)
Lee, D.J. ; Dept. of Electr. & Comput. Eng., Brigham Young Univ., Provo, UT ; Yuchou Chang ; Archibald, J.K. ; Greco, C.G.

Machine vision has become an important non-destructive visual inspection technology for automation in the past two decades. Using machine vision for production automation can reduce operating costs and increase product value and quality. For agricultural products, color is often a good indicator of product quality and maturity. This paper presents a novel image-dependent color quantization technique designed specifically for real-time color evaluation in production automation applications. In contrast with more complex color space conversion techniques, the proposed method makes it easy for a human operator to specify and adjust color-preference settings for different color groups representing distinct quality or maturity levels. The performance of this robust color quantization and image analysis technique in evaluating fruit maturity and detecting skin delamination defects is demonstrated using Medjool date samples collected from field testing.

Published in:

Automation Science and Engineering, 2008. CASE 2008. IEEE International Conference on

Date of Conference:

23-26 Aug. 2008