GreenBuddy AI

GreenBuddy AI 🌱 — Your Local Gardening Companion

🌿 What I Built

GreenBuddy AI is an AI-powered gardening assistant designed to help beginners identify plants, learn how to care for them, plan seasonal planting, and spend more time outdoors.

I built this project for the Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass.

✨ Features

• 🌱 Plant Identification: Upload a plant photo to get likely plant names and model confidence scores.
• 💧 Plant Care Assistant: Get AI-generated guidance for watering, sunlight, soil, and general plant care.

• 🪴 Planting Planner: Generate a planting plan based on your location, growing space, and selected period.
• 🌳 Outdoor Missions: Track simple gardening and outdoor activities to encourage spending more time with nature.
• 🤖 Local AI Assistant: Use a locally running language model through Ollama for gardening questions and suggestions.

🛠️ Tech Stack

• Python and Flask
• HTML, CSS, and JavaScript
• Ollama with Qwen 2.5 3B
• Hugging Face Transformers
• PyTorch and Pillow

💡 Why Open-Source AI?

I wanted to explore how AI could support an everyday activity while keeping the project accessible and transparent.

GreenBuddy AI combines local language-model inference with image classification. Once the required models are downloaded, inference can run locally, reducing dependence on hosted AI APIs and helping keep plant photos and gardening questions on the user's device.

The application still needs Ollama and its models installed, and the plant-identification model must be downloaded before its first use. Some gardening recommendations may need verification for local climate and growing conditions.

🚀 Try the Project

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

The project currently runs locally. Follow the setup instructions in the repository's README to install the dependencies and start the application.

🌱 What I Learned

Building GreenBuddy AI helped me explore how to connect a local language model to a Flask application, integrate an image-classification model, and create a frontend that brings multiple AI features together.

I also learned that AI-generated gardening advice should be treated as a starting point and checked against reliable, location-specific information.

🔮 What's Next?

• Improve plant-identification accuracy and display clearer results.
• Add weather-aware planting recommendations.
• Improve gardening advice for different regions and seasons.
• Add tests, documentation, and more beginner-friendly setup instructions.
• Welcome contributions from the open-source community.

🤝 Contributions

Suggestions, bug reports, and contributions are welcome! Please visit the GitHub repository to explore the code and contribute.

🏆 Challenge

Built for Hacktoberfest 2026 — Week 1: Touch Grass.

Thanks for checking out GreenBuddy AI. Let's use AI to learn about plants, grow greener spaces, and reconnect with nature! 🌿