Identification of Fish Suitable for Consumption with Artificial Intelligence Method Using CNN and SVM Algorithms
DOI:
https://doi.org/10.47709/brilliance.v5i1.5728Keywords:
Image Processing, CNN, SVM, Fish Classification, Fish FreshnessAbstract
In the fishing industry, determining the freshness of fish is essential since it directly affects the quality of consumption and the viability of items on the market. The purpose of this research is to use Convolutional Neural Network (CNN) and Support Vector Machine (SVM) techniques to create an automated system that can determine the freshness of tilapia fish. Identification is carried out by analyzing the visual characteristics of the fish, in particular skin discoloration, which is the main indicator of freshness. The dataset used in this study was obtained from Kaggle, which covers various conditions of tilapia fish with different levels of freshness.
Color conversion into RGB, HSV, and LAB formats to get more accurate color information, image normalization to normalize color intensity, and segmentation to highlight pertinent areas are all part of the pre-processing procedure used to increase the model's accuracy. While SVM is responsible for classifying fish into groups that are either acceptable or unfit for ingestion, CNN is utilized to extract features from fish photos. System testing is carried out by comparing model performance based on classification accuracy.
The experiment's findings demonstrated that the CNN and SVM combination could accurately classify the freshness of tilapia fish, however performance was heavily reliant on the input image's quality. It is anticipated that this technology will replace less effective manual techniques, lessen human observational bias, and expedite the industrial fish freshness assessment procedure. With this artificial intelligence-based system, the fishing industry can improve the efficiency and accuracy of the fish sorting process, which can ultimately improve the quality of products consumed by the public.
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