MeteoShrooms 0.3.1

Dashboard with Key Mushroom Metrics (KMM)

MeteoShrooms

With MeteoShrooms, you can keep track of the perfect conditions for mushroom hunting. We take Open Government Data (OGD) from the official Swiss Meteorological Service MeteoSwiss to calculate key metrics.

📌 NOTE

Contributors Welcome! See Contributing Guidelines for more info...

📌 NOTE

Name changed: I discovered the project meteofunghi, an Italian web site that seems to predict mushrooms occurences with meteo data. I had not previously known of the project; however, since its purpose is very similar to ours and their name is nearly identical, I decided to change the name of our project. Their work deserves respect and they were the first ones to come up with the name meteofunghi, so it is only fair that we adapt. Therefore, the MeteoFungi repository has changed its name to MeteoShrooms. This means that the dashboard is now available at https://meteoshrooms.streamlit.app

Motivation

While hunting mushrooms happens in nature, a lot of the planning can be carried out beforehand.

Where are possible areas to find king boletes?

Should you leave the house right now or are you already too late?

And will your current small basket be large enough or is it time to invest in something bigger?

Meteorological data, time series of past findings as well as other datasets can give us valuable insights.

Concept

Overarching Vision

The main goal is to develop a model to predict mushrooms occurences using OGD data. I suggest starting with meteo data by MeteoSwiss, soil properties (see for example data.geo.admin.ch or opendata.swiss) and using open mushroom observation data from GBIF.

Spatial

Currently, the MeteoShrooms project is focussed on Switzerland, as the Swiss meteorological agency MeteoSwiss has started to provide access to their data as Open Government Data (OGD).

Technical

A main aim is to have sound results calculated efficiently. Therefore, Python Polars is used for calculations, wherever possible. Its LazyFrames optimize the workflow. Data quality is validated through Pandera. Compressed Parquet-Files save storage space and up-/download bandwith.

Currently, the repository hosts all parts of the modelling process: 1. Data Preparation, 2. Model Calculations and 3. Data Presentation:

1. Data Preparation: Download data from OGD sources and bring them into form needed for next step. Ensure data quality,
2. Model Calculations: Perform the calculations to generate predictions from observed data,
3. Data Presentation: Show results on map and/or dashboard. Include documentation of sources and methodology.

flowchart TD
    ground[(Ground Properties)] --> prep
    meteo[(Meteo Data)] --> prep
    mush[(Mushroom Observations)] --> prep
    prep[Prepare Data] --> train
    train[Train AI Model] --> calc
    train --> model@{ shape: lean-l , label: AI Model}
    calc[Calculate Model Predictions] --> present
    present[Present Results]

Visualization

Metrics and time series visulizations are currently hosted as a Streamlit dashboard, so check it out.

Roadmap

For a more detailed overview, see either Issues or the detailed Project Planner, where the project is managed.