Verdantra: An AI-Powered Field Journal That Gets You Outdoors 🌿

A developer built Verdantra, an open-source AI-powered botanical field journal that uses photo analysis to guide outdoor observation rather than replace it. The project pairs a React frontend with a FastAPI backend and a shared AI service interface that routes images either to the Gemini API for online use or to a local Ollama server running the gemma4:e2b model for offline, on-device inference. AI output is labelled unverified, and the repository includes setup instructions, automated tests for backend behavior and output constraints, and documentation; no public demo is deployed yet.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass https://dev.to/challenges/hacktoberfest-week1-2026-10-05 🌿 Verdantra — Look closer outside. Verdantra is an open-source, AI-powered botanical field journal designed to encourage people to step away from their screens and explore the natural world. The idea is simple: take a photo of a leaf, plant, rock, puddle, or another outdoor subject, and use AI to guide your observation rather than replace it. Verdantra is built around an important principle: AI should encourage curiosity about the real world, not replace our own observations. GitHub Repository: https://github.com/codewithvishuuu/verdantra https://github.com/codewithvishuuu/verdantra The project currently runs locally. A public, deployed demo is not available yet. The repository includes setup instructions for running the backend and frontend locally, configuring the AI provider, and testing the application. Explore the complete source code: The repository contains the React frontend, FastAPI backend, AI integration, journal functionality, tests, and individual project documentation files. I built Verdantra using a modern web stack with an emphasis on practical AI integration, privacy awareness, and reliable local development. Technology stack gemma-4-26b-a4b-it gemma4:e2b model The application uses a shared AI service interface so that the observation workflow can work with either provider without requiring separate frontends. For online use, the backend sends the image to the configured Gemini API. For offline use, the backend communicates with the local Ollama server, allowing image analysis without an internet connection once the model is installed and running. The AI instructions emphasize photo-grounded evidence, plain language, explicit uncertainty, and safe observation activities. Predictions are labelled unverified so users understand that AI-generated descriptions can be wrong. I also added automated tests for backend behavior and output constraints. Passing tests verify implementation behavior; they do not guarantee that every AI-generated observation is botanically correct. Nature exploration should not depend on expensive hardware or a single online AI service. Open-source tools made it possible to build and inspect the complete application, combine different AI inference options, and give users a choice between hosted and local processing. Local inference through Ollama also provides an alternative when internet access is unavailable. The hosted option offers a different model and inference path, while the local option keeps image processing on the user's machine. Open innovation makes projects like Verdantra easier for other developers to inspect, learn from, improve, and adapt for their own communities. I hope other contributors will help improve the quality of observations, accessibility, offline usability, and the overall field-journaling experience. This section is optional. I do not have a verified, shareable DevRelay agent-session link to include, so I am leaving it out rather than linking to a nonexistent session. My submission is for the Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass . I have not added any additional partner prize categories because I have not verified which partner categories apply to this project. Built with curiosity, open-source tools, and a little more time outdoors. Verdantra — Look closer outside. 🌱