Democratizing Species Identification: Deep Learning Approach for Image-Based Mangrove Species Classification

Mangrove ecosystems are essential to coastal resilience and biodiversity, yet accurate species identification remains limited by traditional, expert-dependent methods. This study introduces a machine learning-based approach for automated mangrove species identification via a user-friendly web application that allows users to upload images of mangrove features, such as leaves or flowers, for analysis. Focusing on three dominant species found in Pulau Kukup, Johor— Bruguiera cylindrica, Bruguiera gymnorhiza , and Rhizophora apiculata —the system employs advanced image recognition models to classify species from uploaded images. A custom object detection model based on YOLO-NAS, a state-of-the-art convolutional neural network architecture, was trained using a curated and augmented dataset hosted on Roboflow. Uploaded images are processed through an image preprocessing pipeline and passed through the trained model for prediction. Model performance was evaluated using precision metrics across varying confidence thresholds. The resulting average precision (maP) of 54.6% − 89% demonstrates the model’s capability to identify mangrove species with moderate accuracy, minimizing false positives. This work highlights the potential of integrating deep learning and computer vision into biodiversity monitoring tools. By enabling automated species identification from user-submitted images, the system supports broader participation in mangrove research and conservation while laying the groundwork for scalable, AI-driven ecological applications.