Artificial Intelligent Fish Abundance Detector Model for Preserving Environmental Stability Amid Aquatic Sustenance and Fishermen

Abstract

The Indian fishery sector has experienced significant growth and is a crucial source of employment for millions of people in the country. India currently contributes 6.5% of the global fish production and 5% of global fish trade, and the fish production sector contributes 1% to the GDP. Over the years, the fish production has seen a steady increase, with a 17-fold rise from 0.75 MMT in 1950-51 to 12.6 MMT in 2017-18.

Finding fish schools and potential fishing areas consumes a large amount of fuel, particularly in fishing methods like purse-seine and pole and line fishing. Accurately predicting the location of commercial fish aggregations in space and time is essential to reducing fuel consumption and improving fishing practices. Fishers make educated guesses based on technology and experience to locate schools of fish, but satellite images can provide more reliable information for any region worldwide.

This paper explores the potential for Deep learning techniques, GIS, remote sensing, and mapping to improve the development and management of marine aquaculture through global example applications. These tools can help address important issues in marine aquaculture and improve the balance between competing and conflicting uses.