Classification of agricultural kernels using impact acoustic signal processing

Abstract

The quality is the main factor that directly affects the price for many agricultural produces. The quality depends on different properties of the produce. Most important property is associated with health of consumers. Other properties mostly depend on the type of concerned vegetable. For instance, emptiness is important for hazelnuts while openness is crucial for the pistachio nuts. Therefore, the agricultural produces should be separated according to their quality to maintain the consumers health and increase the price of the produce in international trades. Current approaches are mostly based on invasive chemical analysis of some selected food items or sorting food items according to their color. Although chemical analysis gives the most accurate results, it is impossible to analyze large quantities of food items. The impact sound signal processing can be used to classify these produces according to their quality. These methods are inexpensive, noninvasive and most of all they can be applied in real-time to process large amount of food. Several signal processing methods for extracting impact sound features are proposed to classify the produces according to their quality. These methods are including time and frequency domain methods. Several time and frequency domain methods including Weibull parameters, maximum points and variances in time windows, DFT (Discrete Fourier Transform) coefficients around the maximum spectral points etc. are used to extract the features from the impact sound. In this study, we used hazelnut and wheat kernel impact sounds. The success rate over 90% is achieved for all types produces.