An Artificial Intelligence-based Integrated Framework for Oceanographic, Fisheries, and Molecular Biodiversity Data - K, Ashwini; L, Binduja; P, Harshitha; Chandana, C E; N R, Deepak (Journal of Data Engineering and Knowledge Discovery, 2026)
Large amounts of ocean, fisheries, and biodiversity data are collected today from many sources, such as satellites, fishing records, environmental sensors, research articles, and DNA sequencing labs. Even though this information is valuable, it remains scattered in different formats and locations. Because of this breakage, it becomes difficult for fishermen, researchers, and policymakers to access the knowledge they need for sustainable marine planning and decision-making. This project introduces an AI-based unified data platform that brings all ocean-related knowledge into one system. The platform will collect, clean, and organise multi-modal data from satellite imagery, CSV files, scientific literature, and molecular datasets. Fishermen will receive forecasts of safe and high-probability fishing zones. The unified dashboard will provide user-specific outputs. Policymakers will obtain insights into overfishing risk areas and conservation requirements. The goal is to support both economic gains and ecological sustainability. By combining real-time analytics with spontaneous visual tools, this platform will create a bridge between scientific data and field-level decision-making.