Shrimp are widely
consumed as one of the most favorable seafood in
recent years. As worldwide shrimp production
grows, shrimp quality evaluation becomes a
critical task for the seafood and aquaculture
industries. Automatic evaluation of shrimp shape
is critical to improving shrimp quality and
production efficiency. This paper proposes an
Evolution COnstructed (ECO) features based method
to automatically evaluate shrimp shape
completeness. Rather than depends on human
expert-designed features or deliberated image
processing techniques, the proposed method
automatically constructs features that are used by
Adaboost model to detect broken shrimp.
Experimental results show that ECO features based
method obtains an average of 95.1% classification
accuracy with a 0.948 precision rate and a 0.920
recall on our shrimp dataset. Although the
experiment was performed on our shrimp dataset to
prove feasibility, the proposed method can be
easily adapted for other shrimp species.
||Dr. Dong Zhang, Sun Yat-sen University, Guangzhou,
Lillywhite and Beau Tippetts
Zhang, K.D. Lillywhite, D.J. Lee, and B.J.
Tippetts, “Automatic Shrimp Shape Grading
Using Evolution Constructed Features,”
Computers and Electronics in Agriculture, vol.
100, p. 116-122, January 2014.
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