Classification of Small Underwater Species

Classifying small underwater aquaculture species like shrimp and prawns from images is challenging due to poor visibility, lighting variations, occlusions, turbidity, and high computational demands of traditional deep learning models.

A hybrid model using a pre-trained classical convolutional neural network for initial feature extraction, followed by variational quantum circuits to enhance feature representation and classification efficiency on compact data.

This approach could significantly reduce the computational complexity, and could make it possible to do real-time measurements on resource constrained devices. This enables robust monitoring, essential for sustainable fisheries, biodiversity monitoring, and automated marine ecosystem analysis.