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MECT-MobileViT: A Lightweight Fish Weight Prediction Model Based on Dual-View Morphological Feature Fusion and

Yi Wang1, Mingyu Tan1, Jingtao Deng1

  • 1College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.

Animals : an Open Access Journal From MDPI
|July 15, 2026
PubMed
Summary

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A new lightweight AI model, MECT-MobileViT, enables contactless, real-time monitoring of largemouth bass (Micropterus salmoides) body weight. This technology improves precision feeding and yield estimation in aquaculture by overcoming limitations of previous vision-based methods.

Area of Science:

  • Aquaculture technology
  • Computer vision
  • Machine learning

Background:

  • Accurate monitoring of fish traits is crucial for intensive aquaculture.
  • Existing methods like manual measurement are stressful and labor-intensive.
  • Vision-based systems face challenges with feature fusion, underwater noise, and model size.

Purpose of the Study:

  • To develop a lightweight, robust vision-based framework for non-invasive monitoring of largemouth bass.
  • To improve the accuracy of morphological trait and body weight estimation.
  • To enable efficient edge deployment for precision aquaculture applications.

Main Methods:

  • Proposed MECT-MobileViT framework based on MobileViT-xxs.
  • Introduced Morphometric-Guided Multi-Scale Fusion for enhanced shape-weight association.
Keywords:
body weight predictiondeep learningdual-viewmodel lightweightingmulti-scale fusion

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  • Integrated ECA-NL attention block for improved feature robustness and noise suppression.
  • Implemented a three-stage synergistic pruning strategy for model compression.

    • Main Results:

    • The pruned MECT-MobileViT model achieved high accuracy (R²=0.8266, RMSE=16.4201).
    • Achieved significant model compression with only 7.34 M parameters and minimal accuracy loss (<2%).
    • Outperformed mainstream benchmarks on a custom dual-view dataset.

    Conclusions:

    • MECT-MobileViT offers a promising solution for contactless, stress-free weight estimation in aquaculture.
    • The study provides technical advancements in feature fusion, noise reduction, and model compression for aquaculture visual perception.