Problem

The Precision Battle of Indoor Cultivation

Burden of Energy Costs

LED lighting and HVAC account for most operating costs, yet they run in bulk without optimization for each growth stage.

Difficulty Managing Nutrient Solution

Even a slight deviation in EC/pH degrades marketability, but manual measurement and adjustment have their limits.

Scaling Up Without Data

When cultivation recipes rely on human intuition, adding layers and racks fails to replicate quality uniformly.

FarmPath Solution

Cultivation Recipe Automation
That AI Learns

  • Precision LED lighting control — schedules light intensity and photoperiod by growth stage to optimize energy efficiency
  • Drainage & nutrient solution optimization — automatic adjustment based on real-time EC/pH sensor feedback
  • Plant temperature sensors — measures the plant's own temperature rather than the air to prevent climate shock
  • AI analysis integration — RAG-based AI analyzes growth data and proposes improvements
  • Turning cultivation recipes into data — record successful cycles and replicate them at the layer and rack level

RAG-Based AI Farming Support

Evidence-based AI that proposes stage-matched fertilization to prevent waste and diagnoses disease and stress signs early.

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Plant Temperature & PAR Sensors

From photosynthetically active radiation (PAR) to plant temperature — a sensor lineup that observes plant activity directly.

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Expected Outcome

What Changes After Adoption

8~15%
Increase in yield
10~25%
Reduction in inputs
  • Automatic scheduling by growth stage — automatically controls light and nutrient solution to match the growth stage.
  • Recipe-based replicated cultivation — maintains uniform quality with proven cultivation recipes.
  • AI growth data analysis — delivers cultivation insights from accumulated data.

Case studies — National forestry research institute (educational/research smart farm) · 2 university research teams (research smart farm builds)