qfoldit-plant-growth

Plant Growth Model (NPK + light spectrum → morphology & metabolites)

What this skill does

A qualitative heuristic model that, from nitrogen/phosphorus/potassium (NPK) levels and lighting parameters (intensity, photoperiod, spectral composition), computes:

• a growth-rate index (Liebig's law of the minimum — limited by the scarcest resource),
• morphological effects (internode length, branching density, compactness — based on the shade-avoidance syndrome via the R:FR ratio and blue-light fraction),
• qualitative deficiency/excess symptoms for macronutrients,
• a stress-related secondary-metabolite index (anthocyanins/flavonoids).

This is NOT an ML model calibrated on real data. These are transparent, explainable formulas based on well-known plant physiology principles — see references/plant_physiology_basis.md for a full explanation of each relationship. Always present the result as a qualitative/illustrative estimate, not a precise agronomic forecast.

When to use

• Questions about NPK plant nutrition, fertilizer deficiency/excess.
• Questions about the effect of light spectrum/intensity on growth (grow lights, LED spectrum, DLI, R:FR, shade-avoidance syndrome).
• A request to model/visualize how growing conditions will affect plant shape.
• Context — a "virtual lab"/greenhouse in a game/VR demo, where you need to show the effect of environmental parameters on a plant.

How to work

1. Gather the input parameters: N/P/K (in ppm or relative units — if the user doesn't know exact figures, help translate a description like "low nitrogen" into approximate relative levels, explicitly flagging this as an assumption), PPFD and photoperiod (or total DLI directly), and the red/blue/far-red/UV fractions of the spectrum.
2. If the user doesn't know some parameters — use the script's sensible defaults (see --help) and explicitly state which defaults were applied.
3. Run scripts/plant_model.py, read the result.
4. Explain the result to the user in plain language: what the limiting factor is, what symptoms to expect, what the secondary-metabolite index means — using the wording from references/plant_physiology_basis.md.
5. If the user is also using the "qfoldit-l-systems" skill (or asks for a visualization) — add the --emit-lsystem-params flag and pass the resulting preset/angle/iterations/step into qfoldit-l-systems/scripts/lsystem.py to generate an SVG reflecting the computed morphology. Mention that the suggested color (note_stroke_color) needs to be passed manually — lsystem.py doesn't yet support color directly via the CLI.
6. Always end with a reminder: this is an illustrative model; real-world application (a specific greenhouse, specific plant species) requires calibration against real data from that specific installation and species.