Multi-Agent Based Automation of Greenhouse Crop Health Monitoring via Indoor Drones

This workflow automates the continuous health monitoring of high-value greenhouse crops—like berries, leafy greens, or ornamentals—by coordinating indoor drones or crawlers equipped with RGB and multispectral cameras. It eliminates the labor-intensive, inconsistent manual scouting that creates diagnostic delays, allowing nutrient deficiencies, pest infestations, and irrigation issues to impact yield and quality. The operational upside comes from preventing loss in high-margin, dense-planting environments where early correction directly protects revenue and reduces input waste through precise, localized interventions.

Implementation integrates the drone fleet with the greenhouse environmental control system (e.g., Priva, Argus) and fertigation controllers via API. Orchestration is managed by a framework like LangGraph, where specialized agents handle navigation, computer vision analysis using models trained on controlled-environment pathology, and diagnostic logic that cross-references sensor data. Critical controls include confidence scoring to auto-trigger only high-certainty corrections, mandatory human review for ambiguous cases, and immutable logging of all actions for compliance and efficacy analysis. The system requires robust exception handling for navigation failures, data pipeline monitoring, and scheduled model retraining to adapt to new crop varieties.

This workflow eliminates the latency and labor of manual greenhouse scouting by deploying autonomous drones to capture high-resolution RGB and multispectral imagery on scheduled or event-triggered patrols. Computer vision agents process this imagery in near real-time to detect nutrient deficiencies, pest infestations, and irrigation issues, creating a continuous diagnostic layer. The operational upside comes from preventing yield loss through faster intervention and optimizing input use, directly impacting margin in high-value crop production.

Implementation integrates with environmental control systems like Priva or Argus, and fertigation controllers, via a secure API layer. The orchestrator, built on frameworks like LangGraph, manages agent handoffs, exception routing to human agronomists via a review dashboard, and triggers follow-up verification flights. Critical controls include confidence thresholds for autonomous action, audit trails for all prescriptions, and model performance monitoring to trigger retraining when detection accuracy drifts due to new crop varieties.

Phase 1 establishes the core data pipeline: autonomous drones on pre-set routes capture RGB and multispectral imagery, which is ingested into a central orchestration layer (e.g., LangGraph). This layer validates data quality, triggers initial computer vision models for anomaly detection, and logs all raw inputs. The focus is on building a reliable, observable ingestion system and a baseline health dashboard, proving the data flow before adding complex decision logic. This de-risks the project by isolating the hardware and data foundation.

Phase 2 introduces the multi-agent intelligence layer. Specialized agents—for nutrient diagnosis, pest identification, and irrigation analysis—process the validated imagery. A routing agent scores confidence and directs high-certainty issues to integrated control systems (e.g., Priva climate computers, fertigation dosing) via APIs, while low-confidence cases are queued for human agronomist review in the dashboard. This phase delivers the core operational savings by automating diagnosis and initial corrective actions, with clear human-in-the-loop controls for safety and accuracy.

Rollout begins with a single, non-critical crop zone as a sandbox. Here, we deploy the Navigation Agent and a single Vision Agent in shadow mode, logging detections without triggering actuators. This phase validates drone flight safety, sensor calibration, and data pipeline integrity against the greenhouse's SCADA and climate control systems (e.g., Priva, Argus). The goal is to establish a baseline of system behavior and identify any spatial or electromagnetic interference before any automated action is permitted.

Governance is enforced through a centralized Orchestrator Agent that manages all inter-agent communication and acts as a policy engine. It enforces confidence thresholds for automated actions, routes low-confidence detections to a human review dashboard built in Grafana or a custom UI, and maintains a cryptographically signed audit log of every agent decision, data input, and system command. Before full automation, approval gates are implemented where the Prescription Agent's recommended actions (e.g., adjusting nutrient dosing via a fertigation system like NutriSense) require a human sign-off via a mobile alert, creating a supervised learning loop that gradually increases autonomy as trust is earned.