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 aquaculturethis http URLcode are available atthis https URL
View on arXiv@article{gonzález2025_2507.02519,
title={ IMASHRIMP: Automatic White Shrimp (Penaeus vannamei) Biometrical Analysis from Laboratory Images Using Computer Vision and Deep Learning },
author={ Abiam Remache González and Meriem Chagour and Timon Bijan Rüth and Raúl Trapiella Cañedo and Marina Martínez Soler and Álvaro Lorenzo Felipe and Hyun-Suk Shin and María-Jesús Zamorano Serrano and Ricardo Torres and Juan-Antonio Castillo Parra and Eduardo Reyes Abad and Miguel-Ángel Ferrer Ballester and Juan-Manuel Afonso López and Francisco-Mario Hernández Tejera and Adrian Penate-Sanchez },
journal={arXiv preprint arXiv:2507.02519},
year={ 2025 }
}