IMASHRIMP: Automatic White Shrimp (Penaeus vannamei) Biometrical Analysis from Laboratory Images Using Computer Vision and Deep Learning

2025-07-03

AI Summary

To address the labor-intensive, error-prone, and low-efficiency manual morphological analysis in Pacific white shrimp (*Penaeus vannamei*) genetic breeding, this paper proposes a fully automated morphometric analysis method based on RGB-D imaging. Methodologically, we design a multi-module collaborative framework: (1) an improved ResNet-50 for dual-view classification (achieving 0% error rate); (2) a dual-view independent keypoint detection network to enhance robustness under complex postures; (3) integration of ViTPose-based pose estimation (mAP = 97.94%), SVM-based dimensional regression, and pixel-to-centimeter calibration with high accuracy (error = 0.07 ± 0.1 cm); and (4) a human–machine collaborative two-factor verification mechanism to suppress misjudgments. The rostrum detection error rate drops significantly from 12.46% to 3.64%. To our knowledge, this is the first end-to-end, three-dimensional morphological quantification system tailored for aquaculture genetic breeding—establishing a deployable technical paradigm for intelligent selective breeding.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This paper introduces IMASHRIMP, an adapted system for the automated morphological analysis of white shrimp (Penaeus vannamei}, aimed at optimizing genetic selection tasks in aquaculture. Existing deep learning and computer vision techniques were modified to address the specific challenges of shrimp morphology analysis from RGBD images. IMASHRIMP incorporates two discrimination modules, based on a modified ResNet-50 architecture, to classify images by the point of view and determine rostrum integrity. It is proposed a "two-factor authentication (human and IA)" system, it reduces human error in view classification from 0.97% to 0% and in rostrum detection from 12.46% to 3.64%. Additionally, a pose estimation module was adapted from VitPose to predict 23 key points on the shrimp's skeleton, with separate networks for lateral and dorsal views. A morphological regression module, using a Support Vector Machine (SVM) model, was integrated to convert pixel measurements to centimeter units. Experimental results show that the system effectively reduces human error, achieving a mean average precision (mAP) of 97.94% for pose estimation and a pixel-to-centimeter conversion error of 0.07 (+/- 0.1) cm. IMASHRIMP demonstrates the potential to automate and accelerate shrimp morphological analysis, enhancing the efficiency of genetic selection and contributing to more sustainable aquaculture practices.The code are available at https://github.com/AbiamRemacheGonzalez/ImaShrimp-public

Problem

• Automates shrimp morphological analysis using computer vision
• Reduces human error in view and rostrum classification
• Enhances genetic selection efficiency in aquaculture

Innovation

• Modified ResNet-50 for shrimp view classification
• VitPose-adapted module for shrimp key points
• SVM model for pixel-to-centimeter conversion