What question did this study set out to answer?

This study aims to estimate the carapace length at 50% maturity (L50) of swimming crabs using machine learning techniques.

February 11, 2026Open Access

Estimation of the Length at First Maturity of the Swimming Crab (Portunus trituberculatus) in the Yellow Sea of Korea Using Machine Learning

Key Points

This study aims to estimate the carapace length at 50% maturity (L50) of swimming crabs using machine learning techniques.
Applied geometric image augmentation for enhancing image quality.
Utilized HSV color segmentation for analyzing gonad features.
Employed models such as Extreme Gradient Boosting, Support Vector Machine, and Random Forest for classification.
Evaluated model performance based on accuracy, AUC, and TSS metrics.
Machine learning-based L50 estimated at 64.63 mm (±1.73 mm).
Traditional visually derived L50 was 65.47 mm (±2.89 mm).
Ensemble model demonstrated highest predictive performance.
Results suggest that machine learning can improve maturity estimation precision.

Abstract

Swimming crab (Portunus trituberculatus) is a commercially valuable species in the Yellow Sea, where recent fluctuations in resource levels have raised concerns about sustainable management. This study aimed to improve the estimation of the carapace length at 50% maturity (L50) using machine learning techniques, providing a more consistent and reproducible framework for visual maturity classification by standardizing image-based decision processes. Using geometric image augmentation (e.g., rotation, flipping, brightness adjustment), Hue–Saturation–Value (HSV) color segmentation, and algorithms, such as Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), and ensemble models, we classified the maturity of female crabs based on gonad color features. Model performance was evaluated using accuracy, AUC, and the TSS, with the ensemble model showing the highest predictive capability. The machine learning-based L50 was estimated at 64.63 mm (±1.73 mm), yielding a narrower uncertainty range than the visually derived L50 of 65.47 mm (±2.89 mm) under the same macroscopic labeling framework. These results suggest that machine learning-assisted maturity classification can enhance the precision and operational consistency of maturity estimation under a standardized framework, while biological accuracy cannot be confirmed in the absence of an independent reference, such as histological validation.

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