Milkfish Freshness Classification Using Convolutional Neural Networks Based on Resnet50 Architecture

Maulana Malik Ibrahim Al-Ghiffary, Christy Atika Sari, Eko Hari Rachmawanto, Noorayisahbe Mohd Yacoob, Nur Ryan Dwi Cahyo, Rabei Raad Ali

Abstract


Milkfish (Chanos chanos) had become the main commodity in three major cities in Indonesia, contributed at least 77 thousand tons of aquaculture production in 2021. The quality of fish is determined based on the level of freshness carried out in the sorting process, the sorting process is generally done by evaluating physical characteristics of the fish. However, this method is still considered less efficient and economical because the ability to classify the freshness level of fish can vary for each individual. In this study, by utilizing deep learning, a classification method for milkfish freshness level classification with ResNet50 architecture is proposed, the proposed method is purposed to overcome the previously stated problems, thus creating an efficient and economical system. By creating an efficient system, milkfish sorting process can be carried out quicker and more accurately. Using personal dataset divided into four different classes, the proposed method produces excellent result


Keywords


milkfish; CNN; ResNet50; Adam Optimizer

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References


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DOI: https://doi.org/10.26877/asset.v5i3.17017

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Advance Sustainable Science, Engineering and Technology (ASSET)

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