Applying Acoustic Emission and Neural Network to Classify Wheat Seeds From Weed Seeds

In the present study, an expert weed seeds recognition system combining acoustic emissions analysis, Multilayer Feedforward Neural Network (MFNN) classifier was developed and tested for classifying wheat seeds. This experiment was performed for classifying two major important wheat varieties from five species of weed seeds. In order to produce sound signals, a 60o inclined glass plate was used. Fast Fourier Transform (FFT), Phase and Power Spectral Density (PSD) of impact signals were calculated. All features of sound signals are computed via a 1024-point FFT. After feature generation, 60% of data sets were used for training, 20% for validation, and remaining samples were selected for testing. The optimized MFNN model was found to have 500-12-2 and 500-10-2 architectures for “101” and “Shiroodi” wheat varieties, respectively. The selection of the optimal model was based on the evaluation of mean square error (MSE) and correct separation rate (CSR). The CSR percentages for two wheat varieties were 100%. Considering the overall aspects of the results, it can be stated that the developed system was successful enough to correlate the acoustic features with wheat seed type.