This paper is published in Volume-5, Issue-4, 2019
Area
Image Processing
Author
Subramanya G. C.
Co-authors
Neha Misba A. K., Sumedha S., Niharika U. H.
Org/Univ
JSS Science and Technology University, Mysore, Karnataka, India
Pub. Date
22 August, 2019
Paper ID
V5I4-1353
Publisher
Keywords
Artificial ripening, Natural ripening, Digital Image Processing, Android application, Discrete Wavelet Transform (DWT), Grey Level Co-occurrence Matrix (GLCM)

Citationsacebook

IEEE
Subramanya G. C., Neha Misba A. K., Sumedha S., Niharika U. H.. Android application for detection of artificially ripened bananas, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Subramanya G. C., Neha Misba A. K., Sumedha S., Niharika U. H. (2019). Android application for detection of artificially ripened bananas. International Journal of Advance Research, Ideas and Innovations in Technology, 5(4) www.IJARIIT.com.

MLA
Subramanya G. C., Neha Misba A. K., Sumedha S., Niharika U. H.. "Android application for detection of artificially ripened bananas." International Journal of Advance Research, Ideas and Innovations in Technology 5.4 (2019). www.IJARIIT.com.

Abstract

Health and nutrition is one of the leading concerns worldwide. To maintain good health people intake a lot of fruits which contains more amount of nutrition and helps them to remain fit. In order to achieve maximum fruit nutrition benefits, we must ensure that the fruits are not ripened by artificial means. Artificial ripening of fruits is by using calcium carbide (CaC2). Consumption of these types of ripened fruits leads to health problems. In this paper, we propose a method of digital image processing which helps in differentiating between artificially ripened and naturally ripened banana (Musa). This experiment includes sample images of more than 400 bananas of two different species each. Features like contrast, energy, homogeneity, dissimilarity and correlation are considered for classification. An android application is developed which enables the user to identify the method used for ripening. Detection using this method provides 90% accuracy. This detection will help consumers in choosing the right fruits and creating awareness regarding the malnutrition caused due to artificial agents
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