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Non-destructive Detection for Irradiated Apple using Image Processing

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
H.M. Nada, A.A. Arafa, I.F. Tarrad, M. Ashour

H M Nada, A A Arafa, I F Tarrad and M Ashour. Non-destructive Detection for Irradiated Apple using Image Processing. International Journal of Computer Applications 183(24):20-24, September 2021. BibTeX

	author = {H.M. Nada and A.A. Arafa and I.F. Tarrad and M. Ashour},
	title = {Non-destructive Detection for Irradiated Apple using Image Processing},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2021},
	volume = {183},
	number = {24},
	month = {Sep},
	year = {2021},
	issn = {0975-8887},
	pages = {20-24},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2021921609},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper proposes a nondestructive method for detecting irradiated apple rather than the previous destructive method known before such as analytical methods; Chemical, Physical and Biological methods. Image processing technique was applied for rapid and nondestructive detection of irradiated apples. Color intensities, smoothness and uniformities were extracted and analyzed to correlate these color features of apple samples with its values before radiation. ANOVA analysis showed significant differences between both irradiated and un-irradiated apples sample. Linear discriminant analysis (LDA) was utilized for HSV data analysis. Results indicated that it was possible to detect irradiated food with good accuracy using imaging processing technique with an overall success rate of approximately 85%. The proposed method is cheap and less complicated which in turn saves time and effort. Consequently, it overcome the disadvantages of other analytical methods that are complex, costly and destructing the samples.


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Apples, ANOVA, Color evolution, Color Intensity, HSV imaging, Imaging processing, Linear Discriminant Analysis (LDA), RGB imaging.