Frostwise — know your frost dates, plant with confidence

DEV Hacktoberfest AI Challenge — Week 1: Touch Grass.

Every gardener knows the heartbreak: a surprise late frost wipes out weeks of seedlings overnight. Frost dates are the single most important number in gardening, yet most people guess them from a vague zone map or a neighbor's memory.

So I built Frostwise — a hyperlocal garden planner that computes your frost dates from real climate history using TabPFN, the open-source tabular foundation model — then turns them into a planting plan you can actually follow.

Live demo: https://arindewangan.github.io/frostwise/demo/ (real precomputed TabPFN predictions, clearly labeled)
Repo: https://github.com/arindewangan/frostwise
Video: https://youtu.be/ZpVZ8wFglvg

What it does

Type any city — or tap a sample like Chicago, Berlin, Bengaluru, Sydney, Tokyo — and Frostwise:

• pulls 20 years of daily minimum temperatures for your location from the Open-Meteo archive (ERA5 reanalysis),
• runs TabPFN to predict whether your location gets frost at all, and if so, your last spring frost and first autumn frost dates,
• generates a planting calendar for 12 common crops (tomatoes, peppers, kale, garlic, spinach…), with sow windows anchored to your predicted frost dates and honest notes per crop.

No frost where you live? It tells you that too — the model learned it from the data, and you get a year-round growing calendar instead.

How it's built

A Python/Flask backend with a clean single-page frontend. The pipeline:

1. data/build_dataset.py fetches 2005–2024 daily minimums for 80 stratified global locations and derives per-location labels — median first/last frost day-of-year (frost = daily min ≤ 0°C) — plus 17 climate features.
2. At request time, TabPFN performs the whole prediction in-context: a classifier for frost/no-frost, two regressors for the dates — a single forward pass per model, no gradient training.
3. The frontend is vanilla HTML/CSS/JS. The GitHub Pages demo bakes real precomputed TabPFN predictions (labeled with the build date) so it works without a backend.

The honest hard parts

The real challenge was making the AI genuine instead of decorative. A frost date isn't in any API — it has to be derived, so I built the entire label pipeline from raw reanalysis data and had to get the day-of-year statistics right across hemispheres (autumn frost in Sydney falls in a different part of the year than in Berlin). Tropical locations needed genuine "no frost" handling rather than a hardcoded hack — hence the two-stage classifier → regressor design. The other fight was practical: PyTorch's download servers kept timing out, so the environment setup needed patient retries and mirror fallbacks.

What I'm proud of

• A real open-source-AI core: TabPFN inference on real ERA5 data, reproducible end-to-end from the repo — no API keys, no proprietary models, no fake "AI" labels.
• The two-stage design (frost/no-frost classifier → date regressors) that handles everything from Chicago winters to Bengaluru's frost-free climate without special cases.
• A static demo that stays honest: every number is a genuine precomputed inference, clearly labeled as such.
• A UI clean enough that a non-technical gardener can actually use it.

What I learned

TabPFN's in-context learning is remarkably well suited to small, high-signal tabular problems — 80 locations were enough to learn a physically sensible climate→frost mapping. I also learned that the unglamorous half of "AI projects" is data plumbing: deriving trustworthy labels from raw observations mattered more than any model choice. And that honesty in demos (labeling precomputed vs live) costs nothing and buys trust.

What's next

Per-crop frost-damage risk (not just dates), user-saved locations with frost alerts, Southern-Hemisphere season handling in the calendar copy, and a TabPFN uncertainty readout (prediction intervals) so gardeners see the confidence behind each date.

How it uses open-source AI

Frostwise's prediction engine is TabPFN (Apache-2.0, PriorLabs) — an open-source tabular foundation model that performs supervised learning in a single forward pass. We derive frost-date labels from 20 years of open ERA5 reanalysis data (via Open-Meteo's free archive), and TabPFN learns the climate→frost-date mapping in-context at inference time: a classifier predicts frost occurrence and two regressors predict first/last frost day-of-year. No proprietary models or API keys are involved anywhere in the pipeline.

Built with: Python, Flask, TabPFN (open-source), Open-Meteo / ERA5, JavaScript. License: MIT.