Open AccessFish Species Detection Application (FiSDA) in Leyte Gulf Using Convolutional Neural Network
Author(s) -
Gil Gabornes Dialogo,
Larmie S. Feliscuzo,
Elmer A. Maravillas
Publication year - 2021
Publication title -
proceedings of engineering and technology innovation
Language(s) - English
Resource type - Journals
eISSN - 2518-833X
pISSN - 2413-7146
DOI - 10.46604/peti.2021.7892
Subject(s) - convolutional neural network , fish <actinopterygii> , artificial neural network , computer science , artificial intelligence , feature (linguistics) , identification (biology) , similarity (geometry) , epoch (astronomy) , pattern recognition (psychology) , machine learning , fishery , ecology , biology , stars , image (mathematics) , computer vision , linguistics , philosophy
This study presents an application that employs a machine-learning algorithm to identify fish species found in Leyte Gulf. It aims to help students and marine scientists with their identification and data collection. The application supports 467 fish species in which 6,918 fish images are used for training, validating, and testing the generated model. The model is trained for a total of 4,000 epochs. Using convolutional neural network (CNN) algorithm, the best model during training is observed at epoch 3,661 with an accuracy rate of 96.49% and a loss value of 0.1359. It obtains 82.81% with a loss value of 1.868 during validation and 80.58% precision during testing. The result shows that the model performs well in predicting Malatindok and Sapsap species, after obtaining the highest precision of 100%. However, Hangit is sometimes misclassified by the model after attaining 55% accuracy rate from the testing results because of its feature similarity to other species.