Wheat suffers huge losses due to various disasters during the storage stage, which reduces the quality of flour. It is urgent to detect and separate damaged wheat kernels in time. Based on the extraction of four types of wheat collision sound signals, this paper uses digital signal processing methods to extract effective features of the collision sound signals of intact wheat kernels, insect-damaged kernels, moldy kernels and germinated kernels. Finally, BP neural network is used for classification, and a good recognition rate is achieved for the recognition of the three types of wheat. The application results show that BP neural network can better distinguish damaged wheat kernels from intact wheat kernels.
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