Detection Of Empty Hazelnuts From Fully Nuts Using Artificial Neural Network Techniques
Journal Title: International Journal of Farming and Allied Sciences - Year 2014, Vol 3, Issue 2
Abstract
Hazelnut is native to Europe and Asia Minor and one of the products is important that the nuts are grown in over 20 countries worldwide. Major problem in Iran Hazelnut empty farmers before it is complete Overcoming this problem by examining the growth and development, but has not gone completely nuts osteoporosis and the need for new methods are needed to resolve these is important. One of the main reasons may be lack of suitable technology for classification of the quality. In this paper an intelligent separation system is presented based on artificial neural networks (ANNs) for separating empty hazelnuts without breaking them. The components of signal processing system include signal production and recording of its reflections. The produced sounds should be due to twirling of the hazelnuts and not slipping, therefore the spiral surface of the produced sounds has been designed in a way that hazelnut can rotate in all direction on its axis. After running the recorded sounds in MATLAB software, the results are investigated by Neural Network toolbox. The optimal model is selected after several evaluations based on minimizing of mean square error (MSE). A 12-12-1 multi-layer perceptron neural network was used for separation of empty hazelnuts.
Authors and Affiliations
Asiye Doosti, Mohammad Ali Ghazavi, Hojatolah Maghsoudi and Ali Maleki
Keywords
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