TY -JOUR
AU -Adiguna Rahmat Nugraha
AU -Nuryani Widagti
AU -Riyo Wardoyo
AU -I Wayan Eka Darmawan
AU -I Nyoman Surana
AU -Sarah Nuralifah
AU -Devi Shifa Adillah
AU -Alvira Nabilatul Aisyah
AU -Ni Made Nia Bunga Surya Dewi
AU -Edi Kurniawan
AU -Frida Sidik
TI -Mangrove species identification using Convolutional Neural Network
PY -2025
DA -2025-03-20
JO -Taiwania
VL -70
IS -2
SP -2102
EP -2102
UR -https://taiwania.ntu.edu.tw/abstract/2102
AB -Mangroves are unique coastal ecosystems that are rich in biodiversity and have significant ecological value. Identifying mangrove species is important for many applications, such as biodiversity, restoration, and monitoring. As traditional methods are complicated and time-consuming, non-experts need an approach to identify mangroves in a timely and cost-effective manner. In this study, we created a deep learning approach for mangrove species identification based on leaf image recognition. We used digital images of mangrove leaves to identify mangrove species by applying Convolutional Neural Networks (CNN). A dataset of leaf images from 11 ‘true’ mangrove species found in Bali, Indonesia, was developed and divided into 80% for training and 20% for test datasets. About 20% of the training dataset was used for validation. Our results showed an accuracy of 98.86% on validation and 97.16% on a test set of images, promising possibilities for mangrove species identification. The finding indicates that the model effectively identifies mangrove species that are high in diversity and have morphological similarities.
DO -10.6165/tai.2025.70.preview