An AI-Driven Unified Data Platform for Oceanographic, Fisheries, and Molecular Biodiversity Insights
Aqua Insights bridges the gap between complex marine data and actionable intelligence. Built for researchers, conservationists, and policy-makers who need to make sense of oceanographic, fisheries, and biodiversity data — all in one place.
The ocean covers over 70% of the Earth's surface and is home to more than 250,000 known species, yet our ability to integrate and analyze its diverse datasets remains fragmented and siloed. Aqua Insights changes that.
This platform is a next-generation marine science intelligence tool that combines interactive data visualization, AI-powered analysis, and cross-domain correlation to give marine scientists and conservationists an unprecedented view of ocean health, biodiversity trends, and fisheries dynamics — all accessible through a clean, modern web interface.
Whether you're studying the impact of rising sea temperatures on fish populations, identifying an unknown marine species, or correlating biodiversity datasets, Aqua Insights provides the tools to do it efficiently and intelligently.
A cinematic, full-bleed landing page featuring a dramatic ocean photograph. The title Aqua Insights is centered with its tagline — "An AI-Driven Unified Data Platform for Oceanographic, Fisheries, and Molecular Biodiversity Insights" — and a prominent "Enter Dashboard" call-to-action button. The dark overlay and atmospheric design immediately convey the depth and seriousness of the platform.
The Correlation Analysis module is one of the most powerful features of Aqua Insights. Users can:
- Select from Oceanographic Datasets (Temperature Data, Salinity Data, Nutrient Data)
- Select from Biodiversity Datasets (Fish Abundance Data, Species Richness Data, Plankton Biomass Data)
- Define a custom Research Focus query (e.g. "Impact of rising sea temperatures on coastal fish populations")
- Trigger an AI-powered analysis via the Run AI Analysis button
The left sidebar provides seamless navigation between all dashboard modules, with blue AI badges marking AI-powered sections.
Otoliths — tiny calcium carbonate structures inside fish ears — are critical for fish age estimation and ecosystem studies. The Otolith Visualizer offers:
- An interactive canvas model that renders otolith shape with concentric growth rings
- A live Reference Image of the corresponding fish species
- Adjustable sliders for Length (mm), Width (mm), and Growth Rings that dynamically update the visualization in real time
- A side-by-side layout comparing the abstract model with the reference photograph
This tool is invaluable for fisheries researchers doing morphometric analysis without expensive lab equipment.
An immersive full-screen ocean imagery viewer that brings the underwater world to life:
- High-resolution underwater imagery showing coral reefs, fish schools, and biodiversity hotspots
- Interactive data point markers (glowing teal dots) overlaid on the ocean scene, allowing users to click and explore location-specific data
- The viewer serves as a visual anchor for oceanographic datasets, giving geographical and ecological context to the numbers
| Feature | Description |
|---|---|
| 🗺️ Interactive Ocean Viewer | Visualize oceanographic data points on high-res underwater imagery with interactive hotspot markers |
| 🔗 AI Correlation Analysis | Cross-domain analysis of ocean parameters vs. biodiversity datasets, powered by Gemini AI |
| 🐟 Otolith Visualizer | Real-time interactive morphometrics tool for fish otolith shape and growth ring analysis |
| 🔬 AI Taxonomic Assistant | Identify marine species by describing their characteristics — AI returns taxonomy & habitats |
| 📰 AI Ocean Articles | On-demand AI-generated, in-depth scientific articles on marine biology and oceanography |
| 🧭 Unified Dashboard | Single-pane navigation across all tools with a clean dark-themed sidebar |
| Technology | Description |
|---|---|
 |
React framework with App Router, Server Components & Server Actions |
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Statically typed JavaScript for robust, maintainable code |
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UI rendering library powering all interactive components |
| Technology | Description |
|---|---|
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Utility-first CSS for rapid, consistent, responsive styling |
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Accessible, composable component library built on Radix UI |
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Clean, consistent SVG icon library |
| Technology | Description |
|---|---|
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AI orchestration framework for defining flows, prompts, and tools |
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Google's fast, multimodal LLM powering all AI features |
| Technology | Description |
|---|---|
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Performant, flexible form state management |
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TypeScript-first schema declaration and runtime validation |
| Technology | Description |
|---|---|
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Zero-config deployment platform with edge network & CI/CD |
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Source control, version management, and automated deployments |
- Node.js v18 or later (download here)
- npm (comes with Node.js) or any compatible package manager
- A Google AI API Key for Gemini (get one at Google AI Studio)
1. Clone the repository:
git clone https://github.com/GayatriParimiDev/AquaInsights.git
cd AquaInsights2. Install dependencies:
npm install3. Configure environment variables:
Create a .env.local file in the project root:
GOOGLE_GENAI_API_KEY=your_google_ai_api_key_here
⚠️ Important: Never commit your.env.localfile. It is already included in.gitignore.
4. Start the development server:
npm run devOpen http://localhost:3000 in your browser. The app will hot-reload as you make changes.
AquaInsights/
├── src/
│ ├── ai/
│ │ ├── flows/ # Genkit AI flow definitions
│ │ │ ├── cross-domain-correlation.ts
│ │ │ ├── generate-ocean-articles.ts
│ │ │ └── taxonomic-assistant-species-identification.ts
│ │ └── genkit.ts # Genkit + Google AI plugin configuration
│ ├── app/
│ │ ├── dashboard/
│ │ │ ├── correlation/ # Correlation Analysis page
│ │ │ ├── edna/ # eDNA analysis page
│ │ │ ├── otolith/ # Otolith Visualizer page
│ │ │ ├── taxonomy/ # Taxonomic Assistant page
│ │ │ └── articles/ # AI Ocean Articles page
│ │ └── page.tsx # Landing page
│ ├── components/
│ │ ├── features/ # Feature-specific components
│ │ └── ui/ # ShadCN UI base components
│ └── lib/
│ └── actions.ts # Next.js Server Actions (AI calls)
├── public/ # Static assets
├── docs/screenshots/ # README screenshots
├── next.config.ts # Next.js configuration
└── tailwind.config.ts # Tailwind CSS theme configuration
Uses a Genkit flow with a custom datasetSelector tool. The AI is prompted as a marine data analyst, selects the most relevant datasets based on your research query, and performs a statistical correlation analysis returning both raw results and key scientific insights.
A conversational AI flow where users describe morphological characteristics of an unknown marine organism. Gemini 2.5 Flash returns a structured taxonomic classification including Kingdom → Species hierarchy, common names, and typical habitats.
Given a topic (e.g., "Deep-sea hydrothermal vents"), Genkit orchestrates a Gemini prompt that produces a well-structured, scientifically accurate article complete with an introduction, body sections, and conclusion — ready to be cited and shared.
The application is continuously deployed via Vercel on every push to main.
Go to your Vercel Project → Settings → Environment Variables and add:
| Variable | Description |
|---|---|
GOOGLE_GENAI_API_KEY |
Your Google AI Studio API key for Gemini access |
No special build configuration needed — Vercel auto-detects Next.js and applies optimal settings. The next.config.ts includes:
serverExternalPackagesfor Genkit, OpenTelemetry & Handlebars (keeps AI libraries server-side only)- Image optimization for all external image domains used in the platform
This project is licensed under the MIT License — see the LICENSE file for details.
Made with 🌊 by Gayatri Parimi
Protecting our oceans starts with understanding them.