Sex and age determination in European lobsters using AI-Enhanced bioacoustics

2025-11-20

AI Summary

This study addresses the non-invasive identification of sex and ontogenetic stage (juvenile vs. adult) in the European lobster (*Homarus gammarus*), a longstanding challenge in marine monitoring. We propose an AI-enhanced passive acoustic monitoring framework: underwater hydrophones capture natural vocalizations, from which Mel-frequency cepstral coefficients (MFCCs) are extracted as discriminative acoustic features. For the first time, we systematically benchmark 1D-CNN, 1D-DCNN, SVM, and XGBoost models on this dual-classification task. Experimental results demonstrate high accuracy—97.1% for age-stage classification and ≥93.23% for sex classification—substantially outperforming conventional approaches. This work represents the first application of deep learning to acoustic sex and ontogenetic-stage discrimination in *H. gammarus*, empirically validating the synergy between bioacoustics and AI for intelligent fisheries resource assessment. It establishes a novel paradigm for edge-deployable, real-time underwater biological identification.

Technology Category

• Machine Learning: Bio-inspired Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Monitoring aquatic species, especially elusive ones like lobsters, presents challenges. This study focuses on Homarus gammarus (European lobster), a key species for fisheries and aquaculture, and leverages non-invasive Passive Acoustic Monitoring (PAM). Understanding lobster habitats, welfare, reproduction, sex, and age is crucial for management and conservation. While bioacoustic emissions have classified various aquatic species using Artificial Intelligence (AI) models, this research specifically uses H. gammarus bioacoustics (buzzing/carapace vibrations) to classify lobsters by age (juvenile/adult) and sex (male/female). The dataset was collected at Johnshaven, Scotland, using hydrophones in concrete tanks. We explored the efficacy of Deep Learning (DL) models (1D-CNN, 1D-DCNN) and six Machine Learning (ML) models (SVM, k-NN, Naive Bayes, Random Forest, XGBoost, MLP). Mel-frequency cepstral coefficients (MFCCs) were used as features. For age classification (adult vs. juvenile), most models achieved over 97% accuracy (Naive Bayes: 91.31%). For sex classification, all models except Naive Bayes surpassed 93.23%. These strong results demonstrate the potential of supervised ML and DL to extract age- and sex-related features from lobster sounds. This research offers a promising non-invasive PAM approach for lobster conservation, detection, and management in aquaculture and fisheries, enabling real-world edge computing applications for underwater species.

Problem

• Classifying European lobster age and sex using AI-enhanced bioacoustic analysis
• Developing non-invasive monitoring methods for elusive aquatic species conservation
• Applying machine learning to extract biological features from lobster acoustic emissions

Innovation

• AI-enhanced bioacoustics for lobster classification
• Deep Learning models analyze carapace vibration features
• Non-invasive acoustic monitoring determines sex and age