🌱 GreenBuddy AI: Your Local AI Gardening Assistant | Hacktoberfest Week 3

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

I built GreenBuddy AI, an AI-powered gardening assistant that helps beginners spend less time planning their garden and more time growing plants.

Gardening is rewarding, but remembering what to do, when to check plants, and how to plan regular maintenance can become repetitive. GreenBuddy helps simplify that process.

With GreenBuddy AI, users can:

• 🌱 Explore gardening advice tailored to their location and growing space.
• 🪴 Generate practical gardening tasks based on their plants.
• 🤖 Use AI-generated suggestions for activities such as checking soil moisture and inspecting leaves.
• 📅 Assign due dates to tasks and organize their gardening checklist.
• ✅ Save tasks and completion status in the browser, so their checklist remains available after refreshing the page.

The goal is simple: make gardening easier to maintain by reducing repetitive planning.

Demo

GreenBuddy AI currently runs locally on my development machine.

To try it, clone the repository, follow the setup instructions, start Flask and Ollama, and open the local application in your browser.

I haven't included a public deployment link because I haven't confirmed a publicly hosted demo.

Code

GitHub repository:

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

The project is open source, and I welcome feedback, suggestions, and contributions.

How I Built It

I built GreenBuddy AI using Python, Flask, HTML, CSS, JavaScript, and local AI inference.

My AI setup uses Qwen 2.5 3B through Ollama, allowing the application to generate gardening suggestions using a model running locally rather than depending on a paid hosted AI API.

The workflow is straightforward:

1. The user enters their plants, location, and growing space.
2. The Flask backend sends the information and gardening instructions to the local Qwen model through Ollama.
3. The model generates structured gardening task suggestions.
4. The backend validates the returned task data.
5. The user chooses which suggestions to add to their checklist.
6. The browser stores tasks and their completion status using local storage.

I also worked on adding AI-selected due-date offsets so tasks can have different suggested deadlines instead of assigning the same date to every task. This is intended to make the planner more useful for ongoing gardening routines.

I focused on keeping the workflow understandable, beginner-friendly, and practical.

Why Does Open Innovation Matter?

Open innovation made this project possible with tools I could experiment with and adapt to my needs.

Using an open-weight model through Ollama gave me a way to explore local AI inference without making a paid hosted API a requirement for generating suggestions.

It also gave me more control over the prompts, response format, validation, and integration with my Python application.

Open-source tools are especially valuable for learners like me because they make experimentation more accessible. I can inspect the code, understand how the components work together, improve the application, and share those improvements with other developers.

For me, open innovation means being able to learn by building something useful and making that work available to others.

The Lazy Payoff

GreenBuddy AI is designed to reduce the mental effort involved in planning recurring gardening activities.

Instead of starting with a blank checklist, a beginner can request suggestions and choose which tasks to add. Saving tasks in the browser also avoids having to recreate the checklist after every page refresh.

I haven't measured time savings yet, so I won't claim a specific number of minutes saved. My next step is to test the workflow with more gardeners and learn which suggestions and scheduling features are genuinely useful.

The aim is to make gardening routines easier to maintain, one small task at a time.