Species Detection Of Fish, Tracking And Weight Monitoring System Using Machine Learning

Abstract

Our suggested model will demonstrate processing methods for fish identification and tracking automatically
from video sequences. In particular, fish farming operations and global environmental protection call for
substantial research on this topic. Both nature protection and the food business benefit from the computerized
monitoring and counting of various fish species. Fish farms are likely to raise subpar fish to meet the
increased food demand brought on by the world's expanding population. Monitoring fish growth has a
significant influence on the industry that produces aquatic animal food since it helps produce fish products of
higher quality. This model will feature a continuous autofocus, surveillance, and mass prediction system
based on cost-effective monitoring techniques for several fish species. Instead of utilising the conventional
way of taking measurements of the fish, this study uses image analysis to track fish growth in an effort to
increase fish growth rates.