A New EEMD-Based Scheme for Detection of Insect Damaged Wheat Kernels Using Impact Acoustics

Internally feeding insects inside wheat kernels cause significant, but unseen economic damage to stored grain. In this paper, a new scheme based on ensemble empirical mode decomposition (EEMD) using impact acoustics is proposed for detection of insect-damaged wheat kernels, based on its capability to process non-stationary signals and its suppression of mode mixing. The intrinsic mode function (IMF) kurtosis, IMF form factors, IMF third-order Renyi entropies, and the mean of the degree of stationarity were extracted as discriminant features used as the inputs to a support vector machine (SVM) for non-linear classification. In these experiments, 98.7% of undamaged wheat kernels and 93.3% of insect-damaged ones were correctly detected, which indicated the effectiveness of the proposed method for categorizing undamaged wheat kernels from insect-damaged wheat kernels (IDK).