Deep Learning Classification of Reproductive Tissue from Ultrasound: Sex Determination in Red Abalone

May 1, 2026·
Edwin Solares
Edwin Solares
,
et al.
· 1 min read
Abstract
This study develops deep learning classification models to determine the sex of Red Abalone (Haliotis rufescens) from ultrasound images of reproductive tissue, providing a non-invasive approach that reduces stress on animals and operational costs for producers.
Type
Publication
Frontiers in Artificial Intelligence
publications

This work demonstrates that convolutional neural networks can reliably classify biological reproductive tissue from ultrasound imagery, opening a pathway for non-invasive sex determination across aquaculture species.

Deep Learning Computer Vision Aquaculture Ultrasound Classification
Edwin Solares
Authors
Edwin Solares (he/him)
Lecturer in Computer Science & Data Science
I am a computational biologist and data scientist bridging artificial intelligence, evolutionary genomics, and climate-resilient agriculture. My research leverages cutting-edge machine learning and bioinformatics to address global food security challenges in the face of rapid climate change. With publications in high-impact journals including Nature Plants, PNAS, and Genome Research (h-index: 7), I develop tools and methods that advance both computational science and real-world applications.
Authors
et al.