Journal article Open Access
Deepti C.; Arjit Jindal; Prudhvi Reddy; Amrutha D.
<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:contributor>Blue Eyes Intelligence Engineering & Sciences Publication(BEIESP)</dc:contributor> <dc:creator>Deepti C.</dc:creator> <dc:creator>Arjit Jindal</dc:creator> <dc:creator>Prudhvi Reddy</dc:creator> <dc:creator>Amrutha D.</dc:creator> <dc:date>2020-02-29</dc:date> <dc:description>Cultivators and sellers of many high-in-demand fruits traditionally preferred natural ripening after picking. Greed of hefty profits has motivated some of them to artificially hasten the ripening process at the cost of people’s health. Artificial ripening processes tend to degrade the entire quality of the fruit. The focus of this work is to describe a nondestructive method to detect artificial fruit ripening. To aid the detection, the proposed solution utilizes image processing and machine learning techniques to find the artificially ripened fruits. An input fruit image is selected as the test image. The next stage involves comparison of the features (histogram values) of the test image with the image of a naturally ripened one. A smartphone runs an android application to identify artificially ripened fruits. This work specifically concentrates on the commonly preferred Indian Mango and Indian Apple. The developed mechanism has an efficiency of 89-94% in correct detection.</dc:description> <dc:identifier>https://zenodo.org/record/5594209</dc:identifier> <dc:identifier>10.35940/ijeat.C6270.029320</dc:identifier> <dc:identifier>oai:zenodo.org:5594209</dc:identifier> <dc:language>eng</dc:language> <dc:relation>issn:2249-8958</dc:relation> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:source>International Journal of Engineering and Advanced Technology (IJEAT) 9(3) 3833-3839</dc:source> <dc:subject>CNN based Approach, Fruit Ripening, Image Processing, Machine Learning.</dc:subject> <dc:subject>ISSN</dc:subject> <dc:subject>Retrieval Number</dc:subject> <dc:title>A Consumer-Friendly Machine Learning Based Mechanism to Recognize the Quality of Commercially Available Fruits</dc:title> <dc:type>info:eu-repo/semantics/article</dc:type> <dc:type>publication-article</dc:type> </oai_dc:dc>