Using Image Analysis to Predict the Weight of Alaskan Salmon of Different Species

After harvesting, salmon is sorted by species, size, and quality. This is generally manually done by operators. Automation would bring repeatability, objectivity, and record-keeping capabilities to these tasks. Machine vision (MV) and image analysis have been used in sorting many agricultural produc...

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Bibliographic Details
Published in:Journal of food science 2010-04, Vol.75 (3), p.E157-E162
Main Authors: Balaban, MuratO, Ünal Şengör, GülgünF, Soriano, Mario Gil, Ruiz, Elena Guillén
Format: Article
Language:English
Subjects:
accuracy
animal development
Animals
Artificial Intelligence
Automation
Automation - methods
Biological and medical sciences
body conformation
Body Size
Body Weight
Correlation
Correlation analysis
equations
Fish
Fish and seafood industries
Fisheries - methods
food analysis
Food industries
Food science
Fundamental and applied biological sciences. Psychology
hump development
Image analysis
image processing
Image Processing, Computer-Assisted
Machine vision
Mathematical analysis
Oncorhynchus - growth & development
Oncorhynchus gorbuscha
Oncorhynchus keta
Oncorhynchus kisutch
Oncorhynchus nerka
prediction
Regression Analysis
Salmon
Scientific imaging
Size
Sorting
species differences
Vision systems
Weight
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Summary:After harvesting, salmon is sorted by species, size, and quality. This is generally manually done by operators. Automation would bring repeatability, objectivity, and record-keeping capabilities to these tasks. Machine vision (MV) and image analysis have been used in sorting many agricultural products. Four salmon species were tested: pink (Oncorhynchus gorbuscha), red (Oncorhynchus nerka), silver (Oncorhynchus kisutch), and chum (Oncorhynchus keta). A total of 60 whole fish from each species were first weighed, then placed in a light box to take their picture. Weight compared with view area as well as length and width correlations were developed. In addition the effect of "hump" development (see text) of pink salmon on this correlation was investigated. It was possible to predict the weight of a salmon by view area, regardless of species, and regardless of the development of a hump for pinks. Within pink salmon there was a small but insignificant difference between predictive equations for the weight of "regular" fish and "humpy" fish. Machine vision can accurately predict the weight of whole salmon for sorting.
ISSN:0022-1147
1750-3841
DOI:10.1111/j.1750-3841.2010.01522.x