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


Balaban M. O. , Sengor G. F. , Gil Soriano M., Guillen Ruiz E.

JOURNAL OF FOOD SCIENCE, cilt.75, 2010 (SCI İndekslerine Giren Dergi) identifier identifier identifier

Özet

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.

ABSTRACT: 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.

Keywords: image processing, salmon, size, sorting