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

摘要

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).