Kiwifruit Classification using Impact-Acoustic Technique and Artificial Neural Network

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Jannatdost F.; AHMADI MOGHADDAM P.; SHARIFIAN F.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF AGRICULTURAL MACHINERY Year:2020 | Volume:9 | Issue:2 (18) Page(s): 337-346

Download Full-Text

Persian Verion

View:

371

Download:

Cites:

Information Journal Paper

Title

Kiwifruit Classification using Impact-Acoustic Technique and Artificial Neural Network

Author(s)

Jannatdost F. | AHMADI MOGHADDAM P. | SHARIFIAN F. | Issue Writer Certificate

Keywords

Kiwifruit stiffnessQ1

Neural networkQ1

Nondestructive acoustic testQ1

Shelf lifeQ1

Signal processingQ1

Abstract

Introduction: 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. Conclusions: 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.

Cites

No record.

References

No record.

Cite

APA: Copy

Jannatdost, F., AHMADI MOGHADDAM, P., & SHARIFIAN, F.. (2020). Kiwifruit Classification using Impact-Acoustic Technique and Artificial Neural Network. JOURNAL OF AGRICULTURAL MACHINERY, 9(2 (18) ), 337-346. SID. https://sid.ir/paper/384981/en

Vancouver: Copy

Jannatdost F., AHMADI MOGHADDAM P., SHARIFIAN F.. Kiwifruit Classification using Impact-Acoustic Technique and Artificial Neural Network. JOURNAL OF AGRICULTURAL MACHINERY[Internet]. 2020;9(2 (18) ):337-346. Available from: https://sid.ir/paper/384981/en

IEEE: Copy

F. Jannatdost, P. AHMADI MOGHADDAM, and F. SHARIFIAN, “Kiwifruit Classification using Impact-Acoustic Technique and Artificial Neural Network,” JOURNAL OF AGRICULTURAL MACHINERY, vol. 9, no. 2 (18) , pp. 337–346, 2020, [Online]. Available: https://sid.ir/paper/384981/en

Related Journal Papers