Authors :
Rashmi Sandeepani; Erandi Weerasooriya; Sachin Dissanayeke; Kasun Senarathna
Volume/Issue :
Volume 7 - 2022, Issue 11 - November
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3VFKIA9
DOI :
https://doi.org/10.5281/zenodo.7495759
Abstract :
:-The marine food industry in Sri Lanka has
experiencedtremendouscommercialsuccess.Beinganislan
dnation, Sri Lanka’s economy has an impact on both its
domestic and export sectors. Both the social and
economic sectors are impacted by a successful fish catch.
Seafood may considerably contribute to our nutritional
needs because it is a source of high-quality protein,
vitamins, and minerals while being low in saturated fat.
For a very long time, practically every nation on earth
has included fish in considerable quantities in its
citizens’ diets. Smart fish consumption improves people’s
chances of living long, healthy lives. The ability to
recognize, detect, and determine the value of what
individuals use is made possible by the technological
implementation of this unique idea. The Four key
functionalities food fish price prediction, food fish
species identification, whole
fishqualityidentification,andfishfilletqualityidentification
were presented in order to achieve the specific solution.
The suggested approach was implemented using
Multiple Linear Regression and Convolutional Neutral
Network (CNN) algorithms which are EfficientNetB7,
InceptionV3,Resnet50.
Keywords :
Multiple Linear Regression, Convolutional Neu tral Network, quality identification, price prediction.
:-The marine food industry in Sri Lanka has
experiencedtremendouscommercialsuccess.Beinganislan
dnation, Sri Lanka’s economy has an impact on both its
domestic and export sectors. Both the social and
economic sectors are impacted by a successful fish catch.
Seafood may considerably contribute to our nutritional
needs because it is a source of high-quality protein,
vitamins, and minerals while being low in saturated fat.
For a very long time, practically every nation on earth
has included fish in considerable quantities in its
citizens’ diets. Smart fish consumption improves people’s
chances of living long, healthy lives. The ability to
recognize, detect, and determine the value of what
individuals use is made possible by the technological
implementation of this unique idea. The Four key
functionalities food fish price prediction, food fish
species identification, whole
fishqualityidentification,andfishfilletqualityidentification
were presented in order to achieve the specific solution.
The suggested approach was implemented using
Multiple Linear Regression and Convolutional Neutral
Network (CNN) algorithms which are EfficientNetB7,
InceptionV3,Resnet50.
Keywords :
Multiple Linear Regression, Convolutional Neu tral Network, quality identification, price prediction.
