Isolating Healthy Bananas from Unhealthy Ones Based on Feature Extraction and Clustering Method Using Neural Network

Meysam Mans, Hamidreza Fardad, Reza Enteshari and Yaser Mansouri

Modern Applied Science, 2010, vol. 4, issue 11, 51

Abstract: Due to the high sorting speed required during inspection and classification in packing lines, most of the current automatic systems, based on machine vision, are used. Fruit industries are not excluded about this fact. In this paper a method is proposed for detection healthy bananas and defective one. Our algorithm has 4 steps.First, we eliminated background using segmentation methods such as FCM, HCM, Kmeans. Then we extracted the boundaries of a sample banana using edge detection approach. After that, feature from surface of a sample was extracted. Finally, by using a neural network, healthy bananas and defective one was detected.

Date: 2010
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ccsenet.org/journal/index.php/mas/article/download/8037/6041 (application/pdf)
https://ccsenet.org/journal/index.php/mas/article/view/8037 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:masjnl:v:4:y:2010:i:11:p:51

Access Statistics for this article

More articles in Modern Applied Science from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().