Sustainable Maritime Applications with Lightweight Classifier Using Modified MobileNet

The enormously growing demand for seafood has resulted in the over-exploitation of marine resources, pushing certain species to the brink of extinction. Overfishing is one of the main issues in sustainable marine development. To support marine resource protection and sustainable fishing, this study proposes advanced fish classification techniques using state-of-the-art machine learning (ML). Specifically, the proposed method enables the precise identification of protected fish species, among other features. In this paper, we present a system-level optimization of the MobileNet architecture, termed M-MobileNet, designed to operate efficiently on resource-limited hardware environments. Our classifier is constructed by a refined modification of the well-known MobileNet neural network, resulting in a reduction of parameters. Furthermore, we have collected, organized, and compiled an original and comprehensive labeled dataset of 37,462 images of fish native to the Indonesian archipelago. The proposed model is trained on this dataset to classify images of captured fish and accurately identify their respective species. Furthermore, the system provides recommendations regarding the consumability of the catch. Compared to the MobileNet deep neural network structure, our model utilizes only 50% of the top-layer parameters, with approximately 42% GTX 860M utility. This configuration results in achieving up to 97% accuracy of classification. Considering the constrained computing capacity prevalent on many fishing vessels, our proposed model offers a practical solution for on-site fish classification. Moreover, synchronized implementation of the proposed model across multiple vessels can provide valuable insights into the movement and location of various fish species.