Greenhouse Climate and Plant Feedback Analysis

Problem: greenhouse control often lacks a plant view

Traditional greenhouse control relies heavily on environmental thresholds, which can lag when plants are already stressed. For example, leaves may wilt before soil moisture reaches an alarm level, or leaf color may change under high light even if temperature is within range. This skill frames the problem as combining fixed-camera plant morphology (leaf wilting angle, stem uprightness, leaf color changes) with environmental sensor data to create a demand-based control basis.

How it works: images + sensor data + command generation

Input can be jpg/png images or mp4/avi/mov video, preferably covering the full canopy; optional same-time data includes light lux, air temperature, humidity, and soil moisture. The skill runs scripts/smyx_greenhouse_climate_plant_feedback_analysis.py against the API to assess water, light, and heat stress, then outputs action commands with HIGH/MEDIUM/LOW priority. Typical outputs include:
- Irrigation: plant wilting with soil moisture below threshold
- Shading: strong light causing leaf curling
- Fan/wet curtain: high temperature/humidity with heat stress
- Heating: low-temperature stress activation
It does not output PID values or valve opening percentages, so it is best used as an upstream decision reference.

Scope and cautions

This is intended for smart greenhouses, plant factories, and multi-span greenhouse feedback analysis. Results should still be governed by local controller safety policies; historical reports are retrieved from the cloud API rather than local cache. Image quality, canopy coverage, and sensor synchronization all affect the reliability of the generated commands.