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If you are an ASABE member or if your employer has arranged for access to the full-text, click the underlined title below to view. Don't have access to the full text? Click here for options. Corn Kernel Breakage Classification by Machine Vision Using a Neural Network ClassifierPublished by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org Citation: Transactions of the ASAE. 36(6): 1949-1953. @1993Authors: K. Liao, M. R. Paulsen, J. F. Reid, B. C. Ni, E. P. Bonifacio-Maghirang Keywords: Machine vision, Corn breakage, Neural networks A machine vision system was developed to identify corn kernel breakage based on kernel shape profile for automated corn quality inspection. The profile of a corn kernel was sampled into a sequence of one-dimensional digital signals based on its binary image. Shape parameters were selected by analyzing the kernel profile and were sent into a machine learning algorithm to train for a shape membership function of broken versus whole kernel. This system provided successful classifications of 99% for 720 whole kernels and 96% for 720 broken flat kernels, and of 91% for 720 whole kernels and 95% for 720 broken round kernels, respectively. |