Classification of eggshell translucent areas for quality determination using alexnet

dc.contributor.author	Wong, Hsueh Chung
dc.date.accessioned	2021-05-10T04:19:54Z
dc.date.available	2021-05-10T04:19:54Z
dc.date.issued	2018-06
dc.description.abstract	Microbial contaminants are usually the biggest problem faced by egg manufacturer. Bacteria usually penetrate the eggshell through cracks, micro cracks and translucent area are. A fine-tuned Alexnet and a machine vision system were used for inspection of pattern of translucent area. Images 150 cracked egg and 30 intact eggs were taken. To fit into the Alexnet, 1010 images were cropped randomly within the boundary of egg in the size of 227×227×3 in size training and validation data at the ratio of 7:3. The trained network was then evaluated qualitatively using Google Dream Images and quantitatively by visualizing and prioritizing channel with highest activation energy in each convolution layer. Extra 100 cropped images were used as testing images. The trained network was able to detect reject defective egg with accuracy of 91% with false reject rate of 12.5%. The developed system acted as a preliminary study for the development of automated quality determination based on translucent area.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/13374
dc.language.iso	en	en_US
dc.title	Classification of eggshell translucent areas for quality determination using alexnet	en_US
dc.type	Other	en_US

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