Abstract:
In order to realize the automatic classification of corn kernels, this approach collected 840 impact acoustic signals of undamaged kernels, insect damaged kernels and moldy kernels by apparatus of collecting impact acoustic signal, analyzed these signals from the time and frequency domain, extracted the signal features, used the principal component analysis method to reduce the dimensions of the feature data. Finally, BP neural network is used to classify the corn kernels. The classification accuracy of undamaged kernels, insect damaged kernels and moldy kernels were above 90%. The experimental results show that using impact acoustic signal, one can gain a good result in identifying undamaged kernels, insect damaged kernels and moldy kernels. So the approach has a more comprehensive value in practical application and provides a new method for corn kernels quality detection.
