Deep learning-based classification of species in central-southern fisheries in Chile

Abstract

This study introduces a novel deep learning methodology for identifying fish species in central-southern Chile's pelagic and demersal fisheries. Using a dataset of 8,118 high-resolution images encompassing 18 species, two Convolutional Neural Networks (CNNs) were developed: a custom-designed CNN, which achieved an overall accuracy of 86% (95% CI: [0.8355; 0.8826]), and an adapted VGG16 model, which reached 95% (95% CI: [0.9355; 0.9651]) when tested on the same set of 811 images. While both models perform strongly, challenges persist for specific species, particularly Brama australis and Strangomera bentincki, with 33 and 53% classification rates in the VGG16 model, highlighting opportunities for dataset enrichment and algorithmic refinements. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visually interpret the decision-making process of the CNN, providing insight into the regions of the image most relevant to classification. Developed using the Keras API and TensorFlow framework within the R programming environment, our approach underscores the importance of advanced computational tools in enhancing species classification. The results lay the groundwork for future expansions into comprehensive frameworks utilizing computer vision to recognize fish species on board, quantify catches, and detect discards and bycatch. These advancements could significantly benefit Fisheries Observer programs, enhancing enforcement and aiding sustainable fisheries management. Ultimately, this work promotes efficiency and efficacy in monitoring, fostering a sustainable future for marine biodiversity in Chile and potentially other regions and wider marine ecosystems.