NEURAL NETWORK APPROACH FOR BIVALVES CLASSIFICATION

The deep learning approach has demonstrated outstanding performance in detecting and classifying marine organisms such as marine bivalves. Bivalves, such as oysters and clams, have two-valved shells that contain soft-bodied invertebrates. Understanding the diversity of bivalves is essential as they play a vital role in the coastal ecosystem. These species were considered one of the world's threatened species groups. However, marine bivalves face significant exploitation in coastal regions, leading to the endangerment of certain species. Thus, this paper introduces an accurate classification method for marine bivalves. Numerous bivalve images were collected from the coastal area in the northwestern region of Bohol, Philippines, and subjected to image preprocessing techniques to enhance the model's effectiveness. Three deep learning methods, namely MobileNetV2, EfficientNetB4, and ResNet50 architecture, were employed to construct the neural network model for classification. These models utilize complex algorithms to learn and identify patterns and features within the bivalve images, enabling accurate classification. The results of the experiments reveal that EfficientNetB4 achieves higher accuracy in bivalve classification. The implemented model attains a classification accuracy of 97.29%, a recall rate of 97.04%, and precision and F1-score of 97.46% and 97.025%, respectively. In conclusion, the EfficientNetB4 model exhibits significant potential in accurately classifying marine bivalves.