Consulting: AI Automation Blueprint (Cannabis & Controlled Agriculture)
This page summarizes a production‑ready consulting framework for vision + sensors + control logic + robotics in regulated cultivation.
The content is intentionally vendor‑agnostic and facility‑agnostic: it shows the approach, the architecture, and the deliverables—without exposing any third‑party IP.
What you get: an audit‑ready roadmap (URS/CRS), a pilot plan, an integration blueprint for IT/OT, and a scale strategy with measurable KPIs.
Reference Architecture
Our deployments follow a layered model that can be implemented in phases:
Vision Layer
Industrial cameras + edge inference for disease/stress detection, deformation, and early warning alerts.
Sensor Layer
Moisture/EC/temp, CO₂/RH, differential pressure—captured per zone, batch, and cultivar.
AI & Decision
Confidence scoring, rules + ML models, and “human‑in‑the‑loop” approvals for regulated actions.
Robotics
Selective automation: precision pruning/cutting, handling assistance, inspection routines, and repeatable workflows.
Unified Platform
Dashboards, evidence packs, audit exports, and role‑based controls for operations and QA teams.
Industrial Connectivity
PoE switching, resilient networking, and edge compute to keep sensitive data on‑site when required.
Scope of Consulting & Delivery
A1 — Design & Engineering
- URS/CRS and requirements traceability
- Edge + cloud architecture for IT/OT integration
- Documentation aligned with GMP/GLP audit readiness
A2 — Vision Systems
- Per‑room inspection cameras for yellowing, pests, deformation, and growth anomalies
- Automated alerts + confidence scoring
- Edge inference for low latency and privacy
A3 — Sensors & Environment
- Substrate moisture, EC, temperature
- CO₂, RH, differential pressure and zonal conditions
- Calibration + measurement governance
A4 — Platform & Evidence
- Dashboards with KPIs by room, batch and cultivar
- Event logging + evidence packs for audits
- Exports and permissions for QA workflows
A5 — Selective Robotics
- Human‑in‑the‑loop supervision for regulated processes
- Precision pruning and selective removal routines
- Safety review + test protocols
A6 — Industrial Networking
- Industrial PoE switching + edge compute
- Resilient connectivity across zones
- On‑prem logging for confidentiality requirements
Delivery Phases (Pilot → Scale)
- Phase 0 — Concept: KPIs, ROI hypothesis, room selection, security constraints, acceptance gates.
- Phase 1 — Pilot (1 room): install + calibrate, SOPs, first data cycle, evidence workflow.
- Phase 2 — Scale: replicate across rooms with standardised SOPs and governance.
- Phase 3 — Optimisation & Robotics: model tuning, robotics integration, continuous improvement.
Indicative Costs (Facility‑Agnostic)
Budgets depend on room count, camera density, sensor package, and robotics scope. As a reference for a multi‑room controlled‑environment facility:
- CAPEX: typically in the $120k–$180k range for cameras, sensors, edge compute, networking, integration, and documentation.
- OPEX: typically $2k–$5k/month for maintenance, reporting, monitoring, and iterative model improvements.
- Success‑based option: a performance fee can be tied to validated net savings after acceptance gates.
Benefits (What clients typically unlock)
- Early detection of disease/stress with confidence‑based alerts
- Lower operational costs and reduced cycle variability
- Audit‑ready evidence generation for compliance programs
- Scalability and replication to future facilities
Download: Example Proposal (Genericized)
If you want a structured PDF format to share internally, download our example blueprint. It is a template for how we package scope, phases, and budgets.
Open PDF
Want a tailored version for your facility? Use Quick Consult or contact us to schedule a 30‑minute discovery call.