Abstract:
The morphological and color variations of Betta splendens present significant challenges for feature extraction in computer vision applications. Accurate identification is crucial, as the valuation and temperament of these fish specimens are heavily dependent on their morphological characteristics. Although previous studies have explored various computer vision techniques for Betta Splendens classification, there has been little focus on evaluating the performance of different deep learning architectures, especially those optimized for deployment on resource-constrained devices. To address this gap, this study compares the performance of general-purpose deep learning architectures with mobile-specific architectures for Betta Splendens classification. Several widely-used deep learning models were selected for this study based on their availability and relevance. Input feature analysis of the pre-trained architectures was conducted to ensure each model effectively extracts features crucial for classification. The results demonstrate that the InceptionV3 model, fine-tuned with the iNaturalist dataset, achieves the highest accuracy of 0.953 and a recall of 0.9532, outperforming other CNN models. Among mobile-specific architectures, MobileNetV3Small achieved the best results, with 0.90 accuracy and 0.90 recall which is on par with VGG-16 baseline model while having significantly lower hyperparameters. Additionally, as a contribution to the research community, a comprehensive dataset of Betta Splendens fish variants was compiled and made publicly available to support future studies in this domain.
