Year: 2025
Type: article
Abstract: The fisheries industry and aqua-ecology necessitates the precise stratification of fish species, and endeavours to agnize each type of fish based on certain characteristics. Conventional methods engage in manual characterisation which may not be optimal interms of time and impeccable results. While there have been various strategies to analyse and identify the diverse fish species, the flaws of traditional approaches are surpassed through different domains such as machine learning, deep learning and Internet of Things (IoT). The proposed research pivots on stratifying fish species using comparative machine learning algorithms to overcome the problems related to classifying similar species, while significantly ensuring accurate classification results in optimal time. This indagation utilizes the image viewer tool to unsheathe the characteristics of the procured fish type, while building the database to effectuate classification of the different species. The research incorporates the classification juxtapose through three machine learning algorithms such as the Cubic Support Vector Machines (CSVM), Cosine K-Nearest Neighbour (CosKNN) and the ensemble method using bagged trees and Ada boost optimizer to accurately classify fish species based on morphological features extracted from images. The proposed methodology involves the collection of a comprehensive dataset comprising images of different fish species, while sufficiently extracting relevant features, and further entail the application of machine learning algorithms for classification. This research thus contributes to the advancement of automated fish species identification, which can profoundly augment the proficiency and verity of fisheries management, along with enhancing the conservation of aquatic ecosystems. The simulations are carried out in MATLAB, and the results evince the ensemble method yields the zenith of classification accuracy as compared to the other two algorithms.
Author: R.P.Selvam
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: diamond
OpenAlex ID: W4414929001
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