Kiwifruit Classification using Impact- Acoustic Technique and Artificial Neural Network
Fruits and vegetables play an important role in food supply and public health. This group of agricultural products due to high humidity are perishable and most of them (5 to 50 percent) waste during post-harvest operation. Decreasing and minimizing such waste as "hidden harvest" could be an effective way to save food and increase profitability. Despite the surplus of the fruit production in the country, our position in terms of exportation is not commensurate with production, so measurements and grading on the basis of qualitative parameters such as firmness, taste, color, and shape can influence the marketing and export of fruit. In this research, application of an acoustic test is considered to achieve an effective and economic technology in the field to determine the stiffness of kiwifruit in post-harvest step. The aim of this study is to investigate the stiffness index of kiwifruit and provide a classification algorithm in the post-harvest step by using the non-destructive method of processing impact acoustic signals.
Materials and Method
In this research, an acoustic-based intelligent system was developed and the possibility of using the acoustic response to classify kiwifruit into soft, semi-soft and stiff categories was studied. 150 samples of Hayward variety of Kiwifruit was used during the 18 days shelf life in controlled conditions of temperature and humidity. Analyses were done in 9 sets per two days. In each analysis, an acoustic test was done by 48 samples in both free fall condition and fall from a conveyor belt. The feature extraction of acoustic signals in both the time domain and frequency domain has done, then the classification of samples was done by using the Artificial Neural Network. After getting the impact signals of stiff, semi-soft and soft samples, stiffness of kiwifruits identification has done by using acoustic features. The stiffness of kiwifruit samples in this study was measured to be 15.9±4.9 (N) by using the Magnes- Taylor test. Finally, samples were classified into stiff, semi-soft and soft by comparison of maximum force and flux of signals amplitude.
Results and Discussion
The results showed that the features of CF and maximum amplitude in the time domain have high accuracy in kiwifruit classification. The frequency resonances as environmental noises or impact position are out of control in the time domain which causes a decrease in accuracy. So, the ANN by features of time domain has not the acceptable capability to identify the semi-soft samples. The identification of semi-soft samples is not easy because of having same properties of stiff and soft samples. Extracted features of frequency domain have the most capability of correct detection. The optimal network has five neurons in the hidden layer and 0.014782 of mean square error. The accuracy of correct detection of the optimal network was 93.3, 91.3 and 78.3 percent for stiff, semi-soft and soft samples, respectively. Because of using more features in the frequency domain, the classification of all categories was acceptable and identification of semi-soft samples was as good as stiff and soft samples. The results of combined features of time and frequency domain showed that the artificial neural network has less efficiency in comparison with the other two attitudes. The accuracy of identification and classification was decreased by adding the extracted features of the time domain. So achieving the most accuracy in classification is accomplishable just by using the features of the frequency domain. By comparing the results of both free fall and online tests, it is claimed that this research can be industrialized.
Comparison of all results shows that there was no significant difference in the capability of ANN for identification and classification of the sample in three categories. After all, we can use this method in online sorting of kiwifruits by controlling the vector and position of impaction.
Article Type:
Research/Original Article
Journal of Agricultural Machinery, Volume:9 Issue:2, 2019
337 - 346  
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