Abstract
The quality of salted eggs differs in curing process. They need to be tested and graded before factory packaging. The dynamic images of salted eggs were acquired on conveyor. Firstly, preprocessing of color images must be done: the target area of the binary image was determined by mathematical morphology and removal of the object of a small area. According to the binary image is a convex or concave figure, the target region light leaked or not was determined. The effects of leaked region were eliminated by searching for mutation points, fitting salted egg boundary by the Least Square algorithm, labeling the binary image and extracting single target area. Then, six characteristic parameters were extracted in color space, and quality testing model was established by minimum error probability. The experimental results indicated that the detection accuracy reached above 93% and classification efficiency was 5400/h. It proved the model is feasible for salted egg grading. DOI: 10.3965/j.ijabe.20150801.005 Citation: Xu K R, Lu X, Wang Q H, Ma M H. Online automatic grading of salted eggs based on machine vision. Int J Agric & Biol Eng, 2015; 8(1): 35－41.
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