IMASHRIMP: Automatic White Shrimp (Penaeus vannamei) Biometrical Analysis from Laboratory Images Using Computer Vision and Deep Learning
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2025
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| author | González, Abiam Remache Chagour, Meriem Rüth, Timon Bijan Cañedo, Raúl Trapiella Soler, Marina Martínez Felipe, Álvaro Lorenzo Shin, Hyun-Suk Serrano, María-Jesús Zamorano Torres, Ricardo Parra, Juan-Antonio Castillo Abad, Eduardo Reyes Ballester, Miguel-Ángel Ferrer López, Juan-Manuel Afonso Tejera, Francisco-Mario Hernández Penate-Sanchez, Adrian |
| author_facet | González, Abiam Remache Chagour, Meriem Rüth, Timon Bijan Cañedo, Raúl Trapiella Soler, Marina Martínez Felipe, Álvaro Lorenzo Shin, Hyun-Suk Serrano, María-Jesús Zamorano Torres, Ricardo Parra, Juan-Antonio Castillo Abad, Eduardo Reyes Ballester, Miguel-Ángel Ferrer López, Juan-Manuel Afonso Tejera, Francisco-Mario Hernández Penate-Sanchez, Adrian |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02519 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IMASHRIMP: Automatic White Shrimp (Penaeus vannamei) Biometrical Analysis from Laboratory Images Using Computer Vision and Deep Learning González, Abiam Remache Chagour, Meriem Rüth, Timon Bijan Cañedo, Raúl Trapiella Soler, Marina Martínez Felipe, Álvaro Lorenzo Shin, Hyun-Suk Serrano, María-Jesús Zamorano Torres, Ricardo Parra, Juan-Antonio Castillo Abad, Eduardo Reyes Ballester, Miguel-Ángel Ferrer López, Juan-Manuel Afonso Tejera, Francisco-Mario Hernández Penate-Sanchez, Adrian Computer Vision and Pattern Recognition I.2.10; I.4.8 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 |
| title | IMASHRIMP: Automatic White Shrimp (Penaeus vannamei) Biometrical Analysis from Laboratory Images Using Computer Vision and Deep Learning |
| topic | Computer Vision and Pattern Recognition I.2.10; I.4.8 |
| url | https://arxiv.org/abs/2507.02519 |