• Journal of Bio-Environment Control
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• v.20 no.2
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• pp.63-71
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• 2011

Pattern recognition of a cucumber were conducted to detect directly the binary images by using thresholding method, which have the threshold level at the optimum intensity value. By restricting conditions of learning pattern, output patterns could be extracted from the same and similar input patterns by the algorithm. The algorithm of pattern recognition was developed to determine the position of the cucumber from a real image within working condition. The algorithm, designed and developed for this project, learned two, three or four learning pattern, and each learning pattern applied it to twenty sample patterns. The restored success rate of output pattern to sample pattern form two, three or four learning pattern was 65.0%, 45.0%, 12.5% respectively. The more number of learning pattern had, the more number of different out pattern detected when it was conversed. Detection of feature pattern of cucumber was processed by using auto scanning with real image of 30 by 30 pixel. The computing times required to execute the processing time of cucumber recognition took 0.5 to 1 second. Also, five real images tested, false pattern to the learning pattern is found that it has an elimination rate which is range from 96 to 98%. Some output patterns was recognized as a cucumber by the algorithm with the conditions. the rate of false recognition was range from 0.1 to 4.2%.