AI Agentic Workflow for Vineyard Disease Detection and Precision Canopy Management

Powdery mildew, downy mildew, and botrytis can reduce yield by 20-50% if untreated. This workflow uses high-resolution drone imagery and computer vision to detect infections at the sub-canopy level weeks before human scouts, triggering targeted fungicide applications only where needed. This prevents spread, protecting an estimated 3-5 tons per acre of high-value varietals like Cabernet Sauvignon or Chardonnay.

Blanket spraying across entire blocks wastes chemicals and labor. By generating geotagged prescription maps from drone detections, the workflow enables variable-rate application, reducing fungicide and water use by 30-40%. This directly lowers the cost per treated acre while maintaining efficacy and supporting sustainability certifications.

Manual scouting of hundreds of acres is slow and inconsistent. This workflow automates the entire scouting pipeline—from autonomous drone mission planning to AI analysis—freeing viticulturists from routine surveillance. It converts weeks of field walking into hours of dashboard review, allowing skilled labor to focus on strategic canopy management and quality decisions.

Overly dense canopies promote disease and reduce grape quality (Brix, anthocyanins). The workflow analyzes canopy density from NDVI and RGB imagery, generating precise leaf removal and pruning maps. This improves sunlight penetration and air circulation, leading to more consistent ripening and higher-quality fruit that commands premium pricing in competitive markets.

The workflow automatically logs every detection, prescription, and application event with geotagged imagery and timestamps into a centralized record. This creates a defensible audit trail for organic certification, sustainability reporting (ESG), and crop insurance claims, reducing administrative burden and mitigating the risk of costly compliance failures or claim disputes.

For a 500-acre vineyard with an average revenue of $15,000 per acre, a 5% yield protection translates to ~$375,000 in preserved revenue. Combined with 30% input savings (~$150/acre) and labor efficiencies, the workflow typically delivers a full ROI within one growing season, while establishing a scalable data foundation for future precision agriculture investments.

Automates ingestion, stitching, and normalization of high-resolution RGB and multispectral drone imagery into analysis-ready orthomosaics. This pipeline manages versioning, storage, and QC flags, eliminating manual data wrangling and ensuring consistent, georeferenced basemaps for downstream agents. Integrates with platforms like Pix4D or DroneDeploy for scalable processing.

Orchestrates specialized AI agents to process orthomosaics. One agent detects and classifies diseases (powdery mildew, downy mildew, botrytis), while another analyzes canopy density and architecture. A verification agent cross-references detections with soil maps, weather data, and spray records to filter false positives before routing high-confidence anomalies for action.

Converts confirmed detections into actionable outputs. For disease, it generates variable-rate spray prescription maps in ISO-XML or shapefile format. For canopy management, it creates pruning/leaf-removal guidance maps. This component integrates bi-directionally with farm ERP systems (e.g., SAP Agri) and machine control platforms (Raven, Trimble) to sync field context and export executable instructions.

Manages the execution and validation of interventions. Routes prescription maps directly to sprayer controllers and dispatches work orders to ground crews via mobile apps. Schedules follow-up drone flights to compare pre- and post-treatment imagery, automatically quantifying efficacy and flagging areas requiring re-treatment. This closes the operational loop, ensuring input spend delivers measurable results.

Automatically logs every workflow event—from geotagged detection imagery and prescription maps to as-applied data from equipment—into a compliance-ready database. Generates audit reports for regulatory bodies or sustainability certifications (e.g., organic, ESG frameworks), drastically reducing the manual record-keeping burden and supporting defensible claims for premium markets.

A governance layer where agents continuously monitor model performance, data drift, and label quality. Automatically triggers model retraining workflows when new disease strains or grape varieties are detected, or when performance degrades. Ensures the computer vision system remains accurate and reliable across changing seasonal and regional conditions.

Automates mission planning, data ingestion, and orthomosaic generation by connecting to drone control APIs (e.g., DJI, Skydio) and cloud processing services (Pix4D, DroneDeploy). The workflow schedules flights based on disease models and weather, triggers stitching jobs, and pushes analysis-ready geoTIFFs to the vision pipeline, eliminating manual data wrangling and ensuring consistent scouting coverage.

Bidirectional sync with platforms like John Deere Operations Center, Trimble Ag Software, or farm-specific ERPs (SAP Agri). Ingests field boundaries, soil data, and application history to contextualize detections. Exports prescription maps and logs executed treatments back as work records, creating a single source of truth for input tracking, compliance, and cost attribution.

Direct integration with sprayer and spreader consoles (Raven, Trimble, AgLeader) to enable variable-rate application. The workflow converts AI-generated disease and canopy maps into controller-ready ISO-XML or shapefiles, manages wireless transfer to in-cab displays, and can confirm 'as-applied' data feeds back into the system. This closes the loop from detection to chemical on the vine.

Orchestrates human action by routing geotagged detections to mobile field apps (e.g., Fulcrum, FieldAlytics) or custom crew dashboards. Automatically generates scouting or pruning work orders with priority, location, and reference imagery, optimizes routing for multiple crews, and requires digital confirmation of task completion. Ensures rapid response to high-priority zones.

Integrates with IoT-based irrigation controllers (e.g., Lindsay, Netafim) and greenhouse climate systems. Uses drone-derived thermal and NDVI data to identify water stress or excessive canopy humidity (a disease driver). Automatically adjusts zone schedules or triggers ventilation to optimize vine health and reduce disease pressure at a micro-climate level.

Automates regulatory and certification reporting by connecting to blockchain traceability platforms (IBM Food Trust) or sustainability reporting engines. Hashes and records key events—such as sustainable practice verification, organic input compliance, or treatment logs—from drone data to create an immutable, geotagged provenance record for premium wine marketing and audit readiness.