Automated modeling of leaf mangrove by optimizing convolutional neural networks

Abstract

Accurate mangrove species identification is fundamental to biodiversity conservation, ecological monitoring, and climate change mitigation because mangrove ecosystems provide essential ecological services and function as significant carbon sinks. This study proposes a systematic optimization framework for mangrove species identification by jointly investigating dataset size, convolutional depth, and hyperparameter configuration within a convolutional neural network (CNN). Leaf images of seven mangrove species collected in Kolaka Regency, Southeast Sulawesi, were augmented, resized to 128 × 128 pixels, and processed using the NVIDIA DGX A100 supercomputer. The framework systematically optimized the number of convolutional layers, learning rate (LR), batch size, number of epochs, kernel size, activation function (AF), and optimizer, followed by a comprehensive evaluation of their combined effects on classification performance. The optimized CNN achieved 98.93% testing accuracy, 98.71% precision, 98.71% recall, and a 98.86% F1-score. The results demonstrate that the integrated optimization of CNN architecture, dataset size, and hyperparameters substantially improves model robustness and generalization over conventional fixed CNN configurations, providing a practical deep learning framework for automated mangrove monitoring and biodiversity assessment.