Abstract: Seagrass ecosystems are coastal ecosystems with high species diversity, especially fish. Fish diversity determines the abundance of communities based on the number of species. Detection of fish directly (in-situ) and conventionally by catching them requires more energy, costs, and relatively needs time. Therefore a computer vision method is needed that can detect fish well using underwater images. The fish detection model used Masked-Otsu Thresholding, HSV color space with closing techniques in morphological operations. The dataset is in the form of 130 underwater images, divided into 80% training data and 20% testing data. The test results showed a model accuracy value of 0.92, Precision value of 0.84, Sensitivity value of 0.93, and F1 Score of 0.88. With these results, the model could detect fish in the seagrass ecosystem.

Sri Dianing Asri, Indra Jaya, Agus Buono and Sony Hartono Wijaya, “Fish Detection in Seagrass Ecosystem using Masked-Otsu in HSV Color Space” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131253

@article{Asri2022,
title = {Fish Detection in Seagrass Ecosystem using Masked-Otsu in HSV Color Space},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131253},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131253},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Sri Dianing Asri and Indra Jaya and Agus Buono and Sony Hartono Wijaya}
}