July 25, 2024 — Scientists demonstrate that an innovative technology to scan fish otoliths (ear stones) coupled with trained computer models can determine rockfish ages as well as humans, and even more quickly.
This technology, Fourier-transform near-infrared spectroscopy, examines the unique vibrations of molecules in otoliths, especially the way they absorb near-infrared light, to gather detailed chemical information related to fish age.
In this study, NOAA Fisheries scientists used this near-infrared technology in combination with a type of machine learning called deep learning. This is a method of artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions. In this case, scientists used the technology on fish otoliths and integrated it with information on fish length, fish sex, otolith weight, and geographic coordinates to predict rockfish ages.
In the Age and Growth lab, a common method to determine the age of a fish is to extract otoliths from fish and use various techniques. This includes cutting, burning or baking, to make the growth rings in the otoliths more prominent. Scientists then use a microscope to count the rings to estimate the fish’s age. However, using near-infrared technology with deep-learning to estimate fish ages improves precision. It also enhances efficiency compared to ages generated by humans.