MLP Neural Networks in Sex Classification of a High Commercial Fish on the Artisanal Fisheries in Brazil

Abstract

The scarcity of data on artisanal fishing, particularly in tropical South American countries, presents an additional hurdle for fisheries management and marine conservation. While artificial intelligence (AI) has found applications in various domains, including marine sciences, most AI models primarily focus on species identification. Unfortunately, these models often overlook data-limited scenarios. This article examines the potential of an MLPtype ANN model in addressing this gap. The model aims to classify the sex of a dioecious (separate sexes) fish species of high commercial importance, shedding light on its implications for fisheries management. Evaluation of the model's classification performance using precision, recall, f1-score, and accuracy reveals promising results exceeding 80% for both sexes across both training and testing phases. These findings underscore the potential of MLP models in aiding Brazil's fishing sector management in grappling with challenges stemming from data scarcity. By providing efficient information essential for decision-making regarding the management of specific fishing stocks, such models offer valuable insights into effective fisheries management strategies.