A safe hierarchy separating user targets, recipe parameters, measured feedback, actuator calibration and learned sensory models.
Control hierarchy
Direct feedback loops
Closed-loop control is appropriate for directly measured variables such as pH, reservoir level, solution temperature, rotation speed and bulk EC. Individual-ion control requires ion-specific measurement or a constrained mass-balance model validated by chemical analysis.
ExplanationThe feedback error is the target value minus the measured value.
ExplanationThe requested correction combines present and accumulated error, then remains inside declared actuator and safety limits.
Learned sensory mapping
The sensory predictor is trained from completed FlavoRotor experiments. Its input data include genotype, developmental stage, measured environmental history, solution composition and harvest handling. Cross-validation is separated by cultivation cycle to prevent samples from the same run appearing in training and test sets.
Safety constraints
- no automatic dose with an expired channel calibration;
- no correction while the mixing delay is active;
- bounded dose and runtime per event;
- sensor plausibility and redundancy checks;
- fault-safe state after communication loss;
- full event logging with recipe and firmware versions.
State-conditioned treatment
A recipe contains time limits and plant-state conditions. For example, a light phase can begin when a validated imaging model detects the required developmental stage, provided plant-health checks pass and the minimum and maximum calendar limits are respected.
ExplanationThe next plant state depends on the current state, controlled inputs, measured disturbances, genotype and model parameters, plus biological variation.
ExplanationSensors, images and laboratory analyses observe only part of the underlying plant state and include measurement error.