Optimizing Vertical Farming with Real‑Time AI Insights

• Increase utilization by minimizing under‑allocated pockets and preventing overbooking.
• Shorten decision time with live pricing and clear fit/feasibility feedback.
• Improve predictability of yield and cash flow via historical patterns and AI signals.
• Standardize metadata for containers (size, utilities, climate control) to enable search and benchmarking.

• Space precision: Manual planning caused gaps and overlaps; growers lacked a reliable view of L×W×H fit.
• Pricing opacity: Varying rules for m³/day rates, utilities, and duration discounts confused buyers.
• Fragmented data: Inconsistent container specs (insulation, temp range, water) reduced discoverability.
• Ops visibility: Admins had no forward‑looking view of demand, churn risk, or revenue per m³.
• Scale & latency: Real‑time updates across many containers required resilient streaming and caching.

Architecture

• Mobile apps (Flutter) for growers; web console for operators/admins.
• Services (Node.js): pricing, allocation validator, booking orchestrator, telemetry ingest.
• Data: PostgreSQL (metadata, bookings), time‑series store for sensor streams; CDN for media.
• AI: yield forecasting, anomaly detection; model registry and batched retraining.

Key Design Choices

• Edge validation to block impossible dimensions before they hit the API.
• Event‑sourced bookings with idempotent writes; conflict resolution for overlapping holds.
• Cache‑first reads for listings; WebSocket push for availability changes.

Core Flows

• Discover & Filter: Search by size, utilities, insulation, temp control; badges for availability.
• Space Selector: L×W×H sliders with live validation and utilization meter; 2D plan and optional 3D preview.
• Price & Reserve: Auto‑priced quote with breakdown (space %, duration, utilities); soft hold; confirm.
• Admin Oversight: Utilization dashboards, revenue per container, cohort analyses, and CSV/PDF exports.

Feature Matrix

Pricing & Calculations

• Utilization (%) = (Allocated Volume ÷ Total Volume) × 100
• Rental Price = BaseRate_per_m³/day × Allocated_m³ × Days × (1 − DurationDiscount)
• ROI (simple) = (Revenue − (Opex + CapEx/period)) ÷ (Opex + CapEx/period)

Example: Base $4/m³/day, 12 m³ for 14 days, 10% long‑stay discount → $4×12×14×0.9 = $605.

Security & Compliance

• Auth via OAuth2/OIDC; role‑based access (grower, operator, admin).
• Data at rest AES‑256; in transit TLS 1.2+; signed URLs for media.
• Audit logs for pricing changes, holds, and booking confirmations.
• Backups, PITR, and PII minimization (only operational data retained).

Implementation Roadmap

• Phase 1 (0–3 wks): Requirements, IA, UI kit, clickable prototype.
• Phase 2 (4–8 wks): Listings, Space Selector, pricing service, booking holds.
• Phase 3 (9–12 wks): Admin dashboards, exports, alerts; hardening & UAT.
• Phase 4 (13+ wks): AI forecasting v1, multi‑tenant ops, cost optimization.

FAQ

• Can operators set custom rate cards? Yes—by container type, utilities, and duration bands.
• Does the system prevent double booking? Yes—event‑sourced holds with conflict detection.
• Is there offline support? Yes—local cache with queued sync for listings and drafts.