Abstract
The existence of conversion industries to sort and grade hazelnuts with modern technology plays a vital role in export. Since most of the hazelnuts produced in Iran are exported to domestic and foreign markets without sorting and grading, it is necessary to have a well-functioning smart system to create added value, reduce waste, increase shelf life, and provide a better product delivery. In this study, a method is introduced to sort and grade hazelnuts by integrating audio signal processing and artificial neural network techniques. A system was designed and developed in which the produced sound, due to the collision of the hazelnut with a steel disk, was taken by the microphone placed under the steel disk and transferred to a PC via a sound card. Then, it was stored and processed by a program written in MATLAB software. A piezoelectric sensor and a circuit were used to eliminate additional ambient noise. The time-domain and wavelet domain features of the data were extracted using MATLAB software and were analyzed using Artificial Neural Network Toolbox. Seventy percent of the extracted data signals were used for training, 15% for validation, and the rest of the data was used to test the artificial neural network (Multilayer Perceptron network with Levenberg-Marquardt Learning algorithm). The model optimization and the number of neurons in the hidden layer were conducted based on mean square error (MSE) and prediction accuracy (PA). A total of 2400 hazelnuts were used to evaluate the system. The optimal neural network structure for sorting and grading hazelnuts was 4-21-3 (four neurons in input layers, 21 neurons in the hidden layer, and three outputs which are the desired classification). This neural network (NN) was used to classify hazelnut as big, small, hollow, or damaged. Results showed 96.1%, 89.3%, and 93.1% accuracy for big/small, hollow, or damaged hazelnuts were obtained, respectively.
Highlights
Hazelnut (Corylus avellana) is a shrub with nearly 2 m height reaching to 6 m in suitable areas [1].Due to its high export value, the improvement and processing of this product is of high importance.Sorting and grading operation before packaging is one of the most important and decisive factors affecting supply trends and success in the market
VM‐034CY, Panasonic, Osaka, Japan), ansensors, acoustican chamber, piezoelectric sensors, signal was placed under the disk within the acoustic chamber, which was isolated by an insulator to capture circuit, and a disk
Signals are subdivided into different segments and solvedInusing scale factor transforms, different frequencies are analyzed by transmitting signals from the high pass and and function transformation, and the results are obtained as a set of argument frequency data
Summary
Hazelnut (Corylus avellana) is a shrub with nearly 2 m height reaching to 6 m in suitable areas [1]. Ghazanfari et al [7] used artificial neural networks to classify four cultivars of Iranian pistachio nuts according to their 2D shapes. They used the shape recognition methods to classify pistachios into four different classes. Mahmoudi et al [10] developed an acoustic system to classify pistachio They integrated their system with an artificial neural network and obtained an accuracy of 97.3%, 96.7%, and 93.1 % for open, closed, and half-closed shell pistachio nuts, respectively. The number of hidden layer neurons was selected based on multiple tests, and the results of calculating the MSE and correlation coefficient (r). Mean squared error (MSE) for these walnuts were 0.0217 and 0.0185, respectively
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