Design of a Digital Image Processing System for Marine Fish Classification Using Logistic Regression
DOI:
https://doi.org/10.51179/ilka.v3i1.37Keywords:
Image Processing, Classification, Marine Fish, Logistic Regression, HSV Color Histogram, Local Binary Pattern, FlaskAbstract
This research develops a web-based marine fish classification system by applying digital image processingtechniques and the Logistic Regression algorithm. The system is intended to recognize four marine fish species, namely milkfish, mackerel tuna, yellowstripe scad, and threadfin bream, through the combination of color and texture feature representations. Color characteristics are extracted using HSV color histograms, while texture information is obtained using the Local Binary Pattern (LBP) method. The experimental dataset consists of 4,000 fish images, with 3,200 images allocated for model training and 800 images used for testing. The evaluation results indicate that the proposed approach achieves an overall accuracy of 89%, with precision, recall, and f1-score values exceeding 0.85 for most fish categories. The system enables automatic image uploading, feature extraction, and classification via a Flask-based web interface, including the capability to detect images that do not belong to the trained classes. Despite achieving promising results, the system is still affected by limitations related to dataset size and visual similarities among fish species. Future work may focus on increasing data diversity and performing evaluations in real-world environments to enhance system reliability and generalization.
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