Orchestrating Irrigation, Fertigation, and Greenhouse Climate Control

In controlled environment agriculture, the core operational challenge is coordinating interdependent systems—irrigation, fertigation, lighting, HVAC, and CO2—to maintain optimal Vapor Pressure Deficit (VPD) and root-zone conditions. Manual management creates bottlenecks, leading to suboptimal yields, energy waste, and input overuse. A custom automation architecture replaces this reactive guesswork with a closed-loop control system that fuses sensor data, executes precise actions via APIs or browser agents, and continuously optimizes for conflicting goals like humidity control versus irrigation volume.

Implementation requires integrating with legacy greenhouse computers (e.g., Priva, Argus), PLCs, and irrigation controllers, often via custom browser automation for systems lacking APIs. The orchestrator uses decision logic—encoded in LangGraph or custom agents—to resolve conflicts, such as reducing irrigation to manage humidity spikes. Savings arise from reduced labor, precise input application (cutting water and fertilizer use 15-30%), and higher-quality yield through consistent environmental optimization, with full audit trails for compliance and certification.

In controlled environment agriculture, irrigation, fertigation, and climate systems have competing objectives: watering raises humidity, which may require venting that wastes heat and CO2. Manual coordination is slow and suboptimal. A custom multi-agent orchestration layer automates this conflict resolution, continuously balancing VPD (Vapor Pressure Deficit), substrate moisture, and nutrient EC/pH. The business value is direct: 15-25% reductions in water and energy use, improved crop consistency, and higher yield per square foot by maintaining the ideal growth zone without human latency.

Implementation integrates with existing greenhouse climate computers (e.g., Priva, Ridder), irrigation controllers, and fertigation skids via APIs or browser agents for legacy systems. The orchestration engine, built on frameworks like LangGraph, uses predefined policy rules and reinforcement learning to make micro-adjustments. Critical controls include approval gates for major setpoint changes, comprehensive audit trails for compliance, and real-time dashboards showing agent decisions and system states. This turns a reactive, siloed operation into a proactive, closed-loop production system.

Phase 1 establishes core data ingestion and a single control loop, such as substrate-moisture-triggered irrigation, to validate sensor integration and basic actuator control via APIs to Priva, Argus, or custom PLCs. This initial win builds stakeholder confidence and funds subsequent phases by demonstrating immediate water savings of 15-25% and labor reduction. The architecture must include robust exception handling and manual override gates to manage rollout risk before expanding autonomy.

Phase 2 introduces the orchestration scheduler, where specialized agents for irrigation, fertigation, and climate (ventilation, heating) are coordinated by a central mediator using LangGraph or a custom rules engine to resolve goal conflicts, like humidity control versus irrigation events. Final phases implement continuous optimization using reinforcement learning to tune setpoints for yield and energy use, followed by scale-out across multiple facilities. Each phase includes defined observability dashboards and rollback procedures.

A custom orchestration layer automates the high-frequency, conflicting decisions between irrigation, fertigation, and climate systems (vents, heaters, humidifiers). It eliminates manual reconciliation labor and suboptimal siloed control, directly improving crop consistency and reducing water, energy, and fertilizer waste by 20-35%. The architecture integrates sensor fusion from substrate moisture, ambient VPD, and nutrient EC/pH with agentic reasoning to resolve trade-offs, such as delaying irrigation to manage humidity, based on real-time crop-stage models and cost constraints.

Implementation requires a stateful orchestrator, built on frameworks like LangGraph, that ingests data from climate computers, nutrient dosers, and irrigation SCADA/PLC systems. Each specialized agent executes within defined guardrails, with all actions logged to a central system like Autogrow or a custom platform for auditability. Rollout is phased, starting with monitoring and recommendation, then moving to closed-loop control with mandatory human approval gates for nutrient recipe changes or major climate shifts to ensure operational safety and trust.