SoilMate: Grow More, Scroll Less 🌱
An open-source AI gardening assistant built for Hacktoberfest 2026 — Week 1: Touch Grass.
The problem: When learning about gardening keeps us indoors
Imagine this.
You have a few empty pots on your balcony. You want to grow coriander, mint, or maybe some spinach. You are excited to start your first little garden.
So you pick up your phone and search for a beginner's guide.
One video leads to another. A gardening blog recommends one soil mix, a forum recommends something completely different, and suddenly you're comparing fertilizer ratios instead of planting seeds.
An hour passes. You have learned a lot about gardening, but you haven't actually started gardening.
That got me thinking: What if technology could help us spend less time learning about the outdoors and more time actually experiencing it?
That is the idea behind SoilMate.
Built for the Hacktoberfest DEV Open-Source AI Challenge — Week 1, themed Touch Grass — SoilMate turns the intention to start gardening into a practical plan of small, achievable outdoor activities.
Its guiding principle is simple:
Grow More. Scroll Less.
🌿 Introducing SoilMate
SoilMate is an AI-powered gardening companion designed to help beginners turn a small outdoor space into a starting point for growing plants.
Instead of giving users an endless conversation about gardening, the goal is to help them answer a few practical questions:
- What could I grow in my available space?
- How much sunlight do my plants need?
- What should I do first?
- What is my next gardening task?
- How can I build a consistent habit of spending time outdoors?
The experience starts with a garden profile. Based on the available gardening reference data and the selected garden conditions, SoilMate helps produce a planting plan and a sequence of practical activities.
The objective isn't to keep someone chatting with AI all afternoon. It is to help them get ready, step outside, and do something with their hands.
☀️ From a garden profile to seven days of action
Gardening can feel overwhelming when you are starting from zero. SoilMate breaks the process into smaller steps.
A seven-day plan can guide a beginner through activities such as:
Day 1 — Observe your space
Spend time outside and identify where direct sunlight falls. Notice shade, wind, and the conditions around your pots.
Day 2 — Prepare your containers
Check container size, drainage holes, and the space available for roots to grow.
Day 3 — Prepare the growing medium
Inspect the potting mix and prepare it according to the requirements of the selected plants.
Day 4 — Start planting
Sow seeds or transplant suitable plants, following their spacing and depth requirements.
Day 5 — Establish a watering routine
Check soil moisture and learn to water according to the plant's needs rather than following a rigid schedule.
Day 6 — Observe and care
Look for changes in the soil and plants. Check moisture, inspect leaves, and make small adjustments where necessary.
Day 7 — Reflect and plan ahead
Record what you observed, review your progress, and decide what to do during the next week.
These activities are a starting template, not a universal planting calendar. Actual sowing, watering, and growing recommendations must reflect the selected plant and relevant local conditions.
The important part is that every step encourages a real-world action.
📵 Outdoor Mode: Your phone should get out of the way
A gardening assistant should be useful when someone is standing next to a pot, not only when they are sitting at a desk.
That is the thinking behind SoilMate's proposed Outdoor Mode.
Rather than presenting a long article or an endless AI conversation, Outdoor Mode focuses on the task that matters right now.
The design goals are straightforward:
- Large, easy-to-tap controls.
- High contrast for outdoor visibility.
- One clear task at a time.
- Short, actionable instructions.
- Minimal distractions and unnecessary navigation.
The ideal interaction is simple: check the task, put the phone down, and get gardening.
Insert a genuine screenshot of Outdoor Mode here once the feature is implemented and tested.
🧠 Why open-source AI?
The open-source AI part of this project is not just a technology choice. It shapes how SoilMate can be understood, improved, and extended.
The challenge asks us to build something with open-source AI at its core and explain why open innovation matters. For SoilMate, that means making the model integration replaceable and keeping the gardening logic inspectable.
1. A model should be replaceable
AI models change quickly. A gardening assistant should not be permanently tied to one proprietary model or provider.
By keeping the inference integration separate from the recommendation engine, SoilMate can support compatible open-weight models as provider availability, quality, and cost change.
This separation also makes it easier for other developers to experiment with different models and compare their outputs.
2. Gardening facts should not depend entirely on a language model
A language model can explain information in a friendly way, but it can also produce incorrect or unsupported advice.
SoilMate's intended architecture combines curated gardening reference data with deterministic application logic and hosted AI inference.
The application can first identify suitable candidate plants using factors such as sunlight, available space, container requirements, and gardening experience. The AI can then help turn that grounded context into an understandable plan.
This separation matters: the model helps explain and organize recommendations; it should not be treated as the source of botanical truth.
3. A useful fallback matters more than a perfect demo
Hosted inference can fail. Free-tier quotas can run out, a model can become unavailable, and network requests can time out.
SoilMate is designed to provide a deterministic fallback using its available gardening data and recommendation rules.
If AI generation is unavailable, the application should communicate that limitation and offer a rule-based plan where sufficient verified data exists.
A fallback is not a claim that every recommendation is correct. Its quality still depends on the underlying reference data and rules.
4. Open innovation makes experimentation possible
An open implementation lets contributors inspect the recommendation logic, improve the data, add regional gardening knowledge, test alternative models, and challenge incorrect assumptions.
That is valuable for a project like this because local growing conditions vary, and no single model should be treated as the final authority on gardening.
Open innovation means people can understand the system, question its decisions, and help make it better.
🏗️ How SoilMate works
The intended architecture separates the gardening knowledge, recommendation logic, AI integration, and user interface.
Garden profile
|
v
Input validation
|
v
Curated gardening reference data
|
v
Deterministic plant ranking
|
v
Hosted open-weight AI
|
v
Structured plan validation
|
+---- Valid output ----> Gardening plan
|
+---- AI failure ------> Rule-based fallback
|
v
Outdoor tasks and journal
The project uses a Python-based web application approach, with Streamlit as the intended interface where compatible with the final implementation.
The main technical responsibilities are:
- Gardening data: structured plant information and growing requirements.
- Recommendation engine: transparent rules for ranking candidate plants.
- AI service: hosted inference, prompt construction, response parsing, and error handling.
- Validation: structured input and output validation.
- User experience: the planting plan, task checklist, and outdoor-focused interface.
- Testing: automated checks for data integrity, recommendation behavior, and failure handling.
The production application is intended to be accessible through a public hosted URL. AI inference therefore uses a hosted endpoint rather than requiring visitors to install a model or run a local AI server.
This has a trade-off: hosted AI requires internet access and is subject to the provider's quotas and availability. Open-weight does not automatically mean unlimited, free, or fully offline.
Insert a genuine architecture diagram or application screenshot here.
🔒 Privacy and user control
Gardening preferences do not need to become another source of unnecessary data collection.
SoilMate's intended privacy-first approach avoids requiring an account for the basic experience. Where feasible, plans and journal entries can remain in browser storage, with a JSON export and import option for users who want a backup.
That approach has limitations. Browser-stored data may not synchronize between devices, and clearing browser data can remove it.
Hosted AI also introduces a separate consideration: information included in an inference request is sent to the configured provider. The final application should explain what data is transmitted and avoid sending unnecessary personal details.
Privacy claims should reflect the actual implementation, not just the design intention.
🧪 What I want to improve next
SoilMate is a starting point for exploring a different relationship between AI and everyday life.
Some of the improvements I would prioritize are:
- Better regional growing guidance based on reliable agricultural references.
- More transparent explanations of why a plant was recommended.
- Testing model outputs against deterministic recommendations.
- Better support for different container sizes and sunlight conditions.
- A simpler way to record garden observations over time.
- More testing of the experience in actual outdoor conditions.
I would also like to evaluate alternative open-weight models based on recommendation quality, response time, reliability, and inference cost rather than choosing a model based only on popularity.
🌱 Try SoilMate and contribute
Source code: https://github.com/awejofficial/soilmate
Live demo: https://soilmate.streamlit.app
The repository and demo links above should be published only after they have been verified and the corresponding project is available.
If you are interested in gardening, open-source AI, or building tools that encourage people to spend more time away from their screens, there are several ways to contribute:
- Improve plant data using reliable horticultural sources.
- Add and test regional growing recommendations.
- Improve the deterministic recommendation engine.
- Evaluate compatible open-weight models.
- Improve accessibility and Outdoor Mode.
- Add tests for failure cases and unsupported recommendations.
- Report bugs and suggest ways to make the experience more useful for beginners.
🌍 The bigger idea: AI that helps us look up
It is easy to judge an AI product by how much time people spend interacting with it.
SoilMate explores a different measure of usefulness: whether a short interaction helps someone take a meaningful action outside the screen.
Maybe that action is planting a few coriander seeds. Maybe it is observing how sunlight moves across a balcony. Maybe it is simply learning to care for one small plant.
The action itself does not need to be extraordinary.
The important thing is that technology helps make it happen.
For the Touch Grass challenge, I wanted to explore how open-weight AI, transparent application logic, and accessible gardening knowledge could work together to support that goal.
Because sometimes the best result of using AI is knowing when to put your phone down.
Grow More. Scroll Less. Happy Hacktoberfest! 🌱
Top comments (1)
heelo