DEV Community: Devananda Anish

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Devananda Anish — Sun, 01 Nov 2026 04:15:55 +0000

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Latest post: "RIVET — Environmental Incident Investigator"
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RIVET — Environmental Incident Investigator

Devananda Anish — Wed, 21 Oct 2026 09:03:27 +0000

RIVET uses computer vision and water-quality sensors to investigate suspicious changes in waterways, verify evidence, and generate traceable environmental incident reports.

Inspiration

Environmental changes in waterways can go unnoticed when monitoring depends on isolated measurements or occasional inspections. We wanted to explore how computer vision and intelligent investigation could help identify suspicious changes and direct attention toward situations that need further examination.

What it does

RIVET (Environmental Incident Investigator) is a proposed system that combines OpenCV-based video analysis with water-quality sensor readings. It aims to detect unusual changes, compare visual evidence with sensor measurements, investigate uncertainty through additional analysis, and generate traceable incident reports.

How we built it

The planned prototype uses Python and OpenCV to analyse video over time. Water-quality sensors will provide independent measurements, while an AI agent will select follow-up analyses when observations are incomplete or conflicting. AWS will support the data-processing and storage workflow.

Challenges we expect

Important challenges include distinguishing genuine environmental changes from lighting variations, synchronising video with sensor readings, collecting reliable test data, and reducing false alarms.

Accomplishments we're aiming for

Our goal is to build a working prototype that demonstrates evidence-driven environmental investigation, combines computer vision with sensor measurements, and evaluates its performance against simpler monitoring methods.

What we aim to learn

We aim to investigate whether adaptive visual analysis and sensor fusion can improve environmental anomaly detection, how uncertainty should influence an AI agent's next action, and how to make automated environmental monitoring more transparent and reliable.

I Built PHANTOM Nature Scout: An Open-Source AI Companion for Exploring Nature

Devananda Anish — Fri, 09 Oct 2026 06:23:54 +0000

This is a personal project inspired by the Hacktoberfest Open-Source AI Challenge Week 1 theme, “Touch Grass.”

What I Want to Build

PHANTOM Nature Scout is a concept for an AI-powered nature companion designed to encourage people to spend more time outdoors. It aims to help users observe nature, identify discoveries, and keep a record of what they learn.

Demo

The working demo will be added here after implementation and testing.

Code

The source code repository will be linked here after the project is implemented.

How I Plan to Build It

I plan to use an open-weight AI model for nature-image recognition, with a simple interface and locally stored observations. I will test the model on my Windows laptop and document its actual capabilities and limitations.

Why Open Innovation Matters

Open-source tools can give developers more control over their technology. Depending on the model and its licence, local inference can improve privacy, reduce dependence on external services, and allow experimentation with different models.

What I Learned

I will document what worked, what failed, and what I learned while building and testing the project.