Classification of Aquatic Species in Cultivation Ponds via Image Processing and Machine Learning

Arif Setiawan; Angga Wahyu Wibowo; Pratomo Setiaji; Wiwit  Agus Triyanto; Muhammad Arifin

doi:10.63158/IJAIS.v2.i1.9

Authors

Arif Setiawan Muria Kudus University Author
Angga Wahyu Wibowo Politeknik Negeri Semarang Author https://orcid.org/0009-0009-6975-6328
Pratomo Setiaji Muria Kudus University Author https://orcid.org/0000-0002-6532-0860
Wiwit Agus Triyanto Muria Kudus University Author https://orcid.org/0009-0008-2954-7380
Muhammad Arifin Muria Kudus University Author

DOI:

https://doi.org/10.63158/IJAIS.v2.i1.9

Keywords:

fish classification, underwater image processing, morphometric features, machine learning, intelligent aquaculture

Abstract

Fish cultivation is a vital economic activity for coastal communities, yet traditional farming methods often face challenges such as environmental instability, feeding inefficiencies, and water pollution. Effective monitoring of underwater environments is essential to improve fish quality and farming efficiency. A crucial part of this process is the accurate classification of fish and non-fish objects. This study proposes a method for underwater classification using morphometric feature extraction and machine learning techniques. The research process involves six main steps: (1) preparation of Region of Interest (ROI) detection data, (2) extraction of morphometric features—length (L) and width (W), (3) feature computation, (4) data partitioning for training and testing, (5) classification using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN), and (6) evaluation using a confusion matrix. Among all models tested, the Random Forest algorithm yielded the highest accuracy at 93%, with classification results showing True Positive = 349, False Positive = 28, True Negative = 223, and False Negative = 0. The findings highlight RF’s potential for enhancing automated fish monitoring in smart aquaculture systems.

Author Biographies

Angga Wahyu Wibowo, Politeknik Negeri Semarang

Department of Electrical Engineering
Pratomo Setiaji, Muria Kudus University

Department of Information System, Faculty of Engineering
Wiwit Agus Triyanto, Muria Kudus University

Department of Information System, Faculty of Engineering
Muhammad Arifin, Muria Kudus University

Department of Information System, Faculty of Engineering

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