Non-destructive Identification of Oyster Species is possible from Hyperspectral Images with Machine Learning
Researchers have developed a machine learning approach using hyperspectral imaging to non-destructively identify oyster species. The study focused on distinguishing between Black-Lip rock (BL) and Sydney rock (SR) oysters by analyzing spectral reflectance from their valves. A Partial Least Square Discriminant Analysis (PLS-DA) model achieved 100% classification accuracy, significantly outperforming a Convolutional Neural Network (CNN) model. AI
IMPACT Provides a novel, non-destructive method for species identification, potentially improving seafood traceability and aquaculture.