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Efficient And Non-invasive Grading Of Chinese Mitten Crab Based On Fatness Estimated By Combing Machine Vision And Deep Learning.

Home
Efficient And Non-invasive Grading Of Chinese Mitten Crab Based On Fatness Estimated By Combing Machine Vision And Deep Learning.

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Efficient and Non-Invasive Grading of Chinese Mitten Crab Based on Fatness Estimated by Combing Machine Vision and

Jiangtao Li¹, Hongbao Ye^2,3, Chengquan Zhou^2,3

¹Huzhou Academy of Agricultural Sciences, Huzhou 313000, China.

Foods (Basel, Switzerland)

|June 13, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Computer vision and deep learning offer efficient, non-destructive quality grading for Chinese mitten crabs. This AI-driven approach accurately identifies sex, measures carapace dimensions, and assesses fatness, improving upon traditional manual methods.

Keywords:

Chinese mitten crab Eriocheir sinensis YOLO grading machine learning

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Area of Science:

Aquaculture
Computer Vision
Deep Learning

Background:

The Chinese mitten crab (Eriocheir sinensis) is a valuable seafood facing increasing market demand.
Current manual quality grading methods are inefficient, labor-intensive, and costly.
There is a need for precise, non-destructive grading systems in the aquaculture and food industries.

Purpose of the Study:

To develop an efficient, automated quality-grading system for Chinese mitten crabs using computer vision and deep learning.
To quantify key physiological traits including sex, carapace dimensions, and fatness for quality classification.
To compare the performance of a novel deep learning model against existing methods.

Main Methods:

Utilized a YOLOv5-seg model integrated with an SE attention mechanism for image analysis.

Trained the model on 2282 RGB images of crabs, with data augmentation techniques applied.

Developed an improved conditional factor K for fatness assessment and quality grading.

Main Results:

Achieved 100% accuracy in sex recognition for the crabs.
Obtained a mean Average Precision (mAP) of 0.995 for carapace segmentation, outperforming other variants.
Demonstrated 100% consistency between the proposed automated grading method and manual grading.

Conclusions:

The developed computer vision and deep learning model provides a precise and non-destructive method for grading Chinese mitten crabs.
This technology can significantly enhance the efficiency and accuracy of quality assessment in the seafood industry.
The findings support the implementation of advanced AI solutions for aquaculture and food processing.