Discrimination of Whole From Broken Corn Kernels With Image Analysis

Image Analysis In Conjunction With Statistical Pattern Recognition Was Used To Discriminate Whole Corn From Broken Com Kemels. Multivariate Discriminant Analysis Was Used To Develop Classification Rules To Aid In The Identification Of Whole Kernels And/Or Broken Kernels. Twelve Parameters Describing The Shape And Size Of Com Kernels Were Considered As Discriminators. A Stepwise Discriminant Analysis Was Performed To Identify Different Subsets Of The 12 Parameters To Consider As Discriminators. Subsets Of 7 And 4 Parameters, Along With The Full Set Of 12 Parameters, Were Evaluated For Discrimination Performance. Experiments Were Conducted With Different Optical Settings To Evaluate The Effect Of Image Resolution On The Results Of Discriminant Analysis. All Of The Broken And 98% Of The Whole Com Kemels In An Independent Set Of Unknown Kemels Were Correctly Identified When Using The Procedures Developed.