GreenBuddy AI

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

I built GreenBuddy AI, an AI-powered gardening assistant that helps beginners identify plants, get plant-care guidance, and plan a garden based on their location, season, and available growing space.

I wanted to make gardening easier for people who want to grow plants at home but don't always know which plants they have, how to care for them, or what they can grow in limited spaces like balconies.

GreenBuddy AI combines plant image identification with AI-generated gardening guidance to make getting started less intimidating.

The potential customers are beginner gardeners, balcony gardeners, and people who want practical help caring for their plants. In the future, the project could offer personalized gardening plans or additional plant-care features as a possible side hustle. I'm still exploring this opportunity and validating what users find most useful.

Demo

Try it locally: Run GreenBuddy AI on your computer to explore its features.

Project post: https://dev.to/amlan24/greenbuddy-ai-3259

The application is currently being tested locally, so a public deployed demo is not available yet.

Code

GitHub repository: https://github.com/AMLAN-07/GreenBuddyAI

The repository contains the source code for the project. Feedback is collected through an in-app form and saved locally for review.

How I Built It

I built GreenBuddy AI using Python and Flask for the backend, with HTML, CSS, and JavaScript for the user interface.

The project uses an open plant-identification model from Hugging Face to analyze plant images. I also use local AI tooling with Ollama and the Qwen 2.5 3B model for gardening assistance.

The main features include:

• Plant identification:

• Plant-care guidance: Get assistance with everyday gardening questions.

• Gardening planner: Explore planting suggestions based on location, season, and growing space.
• User feedback: Collect feedback through a form and save responses to a CSV file.

I tested the application with another person and added a feedback workflow so I can collect suggestions and improve the project based on real usage.

Why Does Open Innovation Matter?

Open innovation made it possible for me to build an AI-powered application without training a large model from scratch or relying entirely on a closed, hosted AI API.

Using open models and tools gives me more control over experimentation and makes it easier to understand how the different parts of my application work together.

Local inference also offers a path toward keeping gardening questions and other inputs on the user's computer when the relevant feature runs entirely locally. Some features, such as downloading models or accessing online resources, may still require an internet connection.

For me, open innovation means that an individual developer can take existing AI research and turn it into something practical, affordable to experiment with, and open to future improvements.

My Agent Session

I explored AI-assisted development tools while building and improving GreenBuddy AI.

I haven't included a saved DevRelay agent session link here because I don't yet have a verified session URL to share.

My First User

I tested GreenBuddy AI with another person and implemented a feedback form to collect suggestions. The feedback is saved locally in user_feedback.csv.

The tester suggested adding a new feature. This helped me recognize the importance of building around user needs instead of relying only on my own assumptions.

I'm reviewing the suggestion to decide which improvement to implement next.

There has been no confirmed payment from this test. My current goal is to validate the problem, improve the application, and understand whether it provides enough value for people to use regularly.

Prize Categories

I'll select the partner prize categories that match the challenge's official rules and the tools actually used in my project before publishing.