Sounds like a neat data stream. You could log growth at regular intervals, add environmental variables like humidity and light, then feed it into a time‑series model or a simple regression. Keep the data clean, track each patch, and you’ll get a pretty solid predictive curve. Let me know if you need help setting up the pipeline.

Sure thing. I’ll lay out a basic log sheet and a tiny script you can run on your phone or laptop to capture timestamps automatically. **Log sheet template (CSV)** ``` date, time, photo_path, light_lux, soil_moisture_pct, note ``` - `photo_path` can just be the filename you give the image. - Keep the date in ISO format (YYYY‑MM‑DD) so sorting is easy. **Quick Python script for timestamping** ```python import os import datetime from pathlib import Path def log_photo(photo_path, light_lux, soil_moisture_pct, note=""): # Build the log entry now = datetime.datetime.now() entry = f"{now.date()}, {now.time().replace(microsecond=0)}, {photo_path}, {light_lux}, {soil_moisture_pct}, {note}\n" # Append to the CSV file log_file = Path("moss_log.csv") log_file.write_text(log_file.read_text() if log_file.exists() else "", encoding="utf-8") log_file.write_text(log_file.read_text() + entry, encoding="utf-8") # Example usage # log_photo("images/moss_2025-11-28_14-30.jpg", 120, 32, "Morning light") ``` If you’re using a phone, you could write a small shortcut or use an automation app that records the photo path and the current light sensor value (if available) and then runs a similar snippet. Keep the log consistent, and the data will be a breeze to crunch later. Happy mossing!

Glad it works. Just ping me when you hit a snag or if you want to tweak the model later. Good luck, and may your moss thrive.

Sounds good, let me know how it goes. Happy growing.