Volume 17, Issue 1 (JIAEEE Vol.17 No.1 2020)                   Journal of Iranian Association of Electrical and Electronics Engineers 2020, 17(1): 11-21 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mavaddati S, Mavaddati S. Rice Classification and Quality Detection Based on Sparse Non-Negative Matrix Factorization. Journal of Iranian Association of Electrical and Electronics Engineers 2020; 17 (1) :11-21
URL: http://jiaeee.com/article-1-719-en.html
University of Mazandaran
Abstract:   (2771 Views)
Rice classification and detection of its quality as a main field in the modern agriculture is attracted many researchers in recent years. This problem is a major issue in the scientific and commercial fields associated with modern agriculture. Different processing techniques in recent years are applied to recognize various types of agricultural products. There are also several color-based and texture-based features to achieve the desired results in this classification procedure. In this paper, the problem of rice categorization and quality detection is considered using sparse non-negative matrix factorization algorithm. This technique includes non-negative matrix factorization method with sparsity constraint to achieve dictionaries that represent the structural content of rice variety. Also, these dictionaries are corrected in such a way to yield the dictionaries with least coherence values to each other. The results of the proposed classifier based on the learned models are compared with the results obtained from the neural network and support vector machine classifiers. Simulation results show that the proposed method based on the combinational features is able to identify the type of rice grain and determine its quality with high accuracy rate.
 
Full-Text [PDF 890 kb]   (1095 Downloads)    
Type of Article: Research | Subject: Communication
Received: 2018/11/12 | Accepted: 2019/08/10 | Published: 2020/03/17

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This Journal is an open access Journal Licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. (CC BY NC 4.0)

© 2024 CC BY-NC 4.0 | Journal of Iranian Association of Electrical and Electronics Engineers

Designed & Developed by : Yektaweb