Abstract
A prototype machine vision station was developed for quality grading of the unique star shape and rich golden colour carambola fruits (Averrhoa carambola L.). The grading criteria were based on the Malaysia’s Federal Agriculture Marketing Authority standards for starfruits export. A Fourier-based shape separations method was developed for shape grading, whereas colour recognition was established using multivariate discriminant analysis. The Wilks’ lambda analysis was invoked to transform and compress the data set comprising of large number of interconnected variates to a reduced set of variates. A robust scaling normalization was introduced in order to achieve invariant to geometrical transformations. Over 200 starfruit samples were inspected by the machine vision system and the results were compared to human judgement. Overall, the vision system was able to classify correctly 100% of the starfruits for shape and 92% for colour. Since the algorithms were implemented in software, the system could be programmed to inspect other fruits and agriculture commodities.
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