Automatic white shrimp (Penaeus vannamei) biometrical analysis from laboratory images using computer vision and deep learning

Abstract

Manual morphological analysis for genetic selection in Penaeus vannamei aquaculture is a slow, error-prone bottleneck. We introduce Imashrimp, an automated system that uses colour and depth images to optimize this task by adapting deep learning and computer vision techniques to shrimp morphology. Imashrimp incorporates two discrimination modules to classify images by the point of view and determine rostrum integrity. These modules function as a "two-factor authentication" (human and Artificial Intelligence) system to validate annotations; this approach reduced metadata annotation errors, cutting point of view classification errors from 0.64% to 0% and rostrum integrity errors from 10.44% to 1.04%. A transformer-based pose estimation module predicts 23 keypoints on the shrimp's skeleton, achieving a general Mean Average Precision of 96.84% and a Percentage of Correct Keypoints of 91.67%. The resulting Two-Dimensional measurements are transformed into Three-Dimensional measurements using a Support Vector Machine regression. By achieving a final Mean Absolute Error (MAE) of 0.08 +/- 0.25 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.