Recognition of Fill and Empty Walnuts Using Acoustic Analysis and Fuzzy Logic

Document Type : Research Article

Authors

1 Department of Biosystems Engineering, Islamic Azad University,Takestan Branch, Takestan, Iran

2 Department of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran

10.22034/jon.2021.1918546.1103

Abstract

To increase the amount of export and marketability of walnuts, a quick, cheap and non-destructive sorting approach should be used. The overall objective is to sort the full, half full and empty walnuts relying on fuzzy logic and sound analysis methods. To sort the walnuts the sound processing technique was used. In this regard, effective parameters on sorting and quality such as: size and shape of walnut were studied. For this purpose, 300 dried walnuts were randomly selected from a walnut orchard for use in experiments. An electronic system consisting of a computer, a microphone, and a mechanical section consisting of a sound chamber were designed to measure the sound intensity of a walnut. At this stage, each walnut was released in three directions: back, side and abdomen 30 cm above the surface of the sound chamber. The sounds were recorded by a microphone with acoustic beats on a sound chamber made of wood and a 45-degree slope. The data from the sound signals were stored in the time domain on the computer and then processed by the MATLAB software. In order to eliminate the ambient noise of signals, Kalman filter algorithm was used to achieve high accuracy and fast convergence. Then these data were analyzed by fuzzy logic method. In this research, WEKA software and J48 algorithm have been used to classify walnuts based on their filling and using the features extracted from the walnut collision with a wooden plate. In order to classify walnuts according to the fullness of walnut kernels, a scientific and innovative index called Full Kernel Index (FK) was used.  The results of this study showed that for classification of walnut, decision trees due to simplicity of structure and creation of fuzzy rules and threshold values of membership functions make fuzzy inference system with high accuracy. The final fuzzy model was presented to classify walnut into two classes with 0.087% separation accuracy and 3 classes with 0.080% separation accuracy.

Keywords


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