Work

A self-learning based explainable AI framework for giant crab sex classification

Year: 2025

Type: article

Abstract: Sexual dimorphism is prevalent in crustaceans, and in crabs, it can be observed in body size, carapace shape, and larger and differently shaped claws in males, among other traits. With the advancement of artificial intelligence (AI), automatic sex classification is now possible by leveraging these differential traits between males and females. Traditionally, most research has relied on analysing the entire crab to classify its sex. Subsequently, studies have focused on specific features, such as the abdominal flap or carapace, which facilitate sex differentiation by humans. However, human intervention creates a significant labelling burden and constrains the exploration of other potentially valuable characteristics for research. In this study, a novel framework is introduced for automatic sex classification in crabs. Unlike traditional approaches that rely heavily on manual labelling and predefined features such as abdominal flaps, our framework minimizes human intervention and enables the model to autonomously highlight image regions most informative for classification. This draws attention to underutilized morphological regions that may be useful for sex classification without requiring prior biological knowledge. Furthermore, the framework provides visual explanations of the model’s decisions, enhancing interpretability. Using this approach, we achieved a classification accuracy of 95.4%, while also pinpointing specific regions contributing to the decision-making process. • Novel self-learning method for crab sex classification requiring less human intervention. • Explainable AI framework for accurate crab sex classification. • AI-driven approach to explore characteristics of marine species.

Source: Computers and Electronics in Agriculture

DOI: 10.1016/j.compag.2025.110989

Authors: Yanyu Chen, Wenli Yang, Scott Hadley, Rafael León Leiva, Quan Bai, Byeong Ho Kang

Institutions: University of Tasmania, Institute for Marine and Antarctic Studies

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,870

Funder: Fisheries Research and Development Corporation