AI Agentic Workflow for Prescriptive Treatment Map Generation from Detection Results

Manual creation of shapefiles or ISO-XML maps from detection polygons is a repetitive, error-prone bottleneck. This workflow automates the entire process: ingesting detection coordinates, applying agronomic rules for product rates and no-spray zones, and outputting controller-ready files. The savings come from redeploying GIS specialists to higher-value tasks and preventing application delays that can worsen crop loss.

Manual map creation often defaults to blanket application rates or simplified zones. The automated workflow applies precise, variable-rate logic based on detection confidence and severity, calibrated to product labels and soil data. This precision targeting minimizes over-application, directly lowering input costs and environmental impact while maintaining efficacy.

The latency between confirming a problem and getting a treatment map onto sprayer hardware is a major operational drag. This workflow triggers automatically upon detection verification, generates the map, and pushes it directly to platforms like John Deere Operations Center or Raven Slingshot. This speed prevents pest or disease spread, protecting yield potential that is lost with every day of delay.

For regulatory compliance (e.g., pesticide reporting, organic certification, sustainability programs), every treatment must be traceable to its justification. This workflow automatically links the final prescription map to the source detection imagery, agronomic ruleset applied, and timestamp, creating a immutable record. This eliminates manual documentation and provides defensible evidence for auditors.

Manual data handling between systems introduces risk of geospatial errors, incorrect rate calculations, or misaligned field boundaries. The automated workflow validates all inputs, performs geometric checks, and flags inconsistencies for review before map generation. This control layer prevents costly mistakes like spraying the wrong field or applying illegal rates, mitigating financial and regulatory risk.

Manually managing prescriptions across a large, dispersed operation is not scalable. This workflow operates as a headless service, processing thousands of detection zones in parallel and outputting standardized maps. It enables a single farm manager or agronomist to oversee precision applications across an entire portfolio, turning a complex operational task into a scalable, automated system.

The core intelligence layer that applies farm-specific business rules to raw detections. It ingests geotagged weed, disease, or nutrient deficiency polygons and automatically determines product type, application rate (e.g., gallons/acre for herbicide, lbs/acre for fertilizer), and no-spray zones (near waterways, sensitive crops). This logic integrates historical application data, soil type maps, and resistance management schedules to generate compliant, optimized prescriptions.

This component translates the agronomic decision into industry-standard file formats (ISO-XML, shapefiles) ready for direct machine upload. It handles format conversion for major equipment controllers (John Deere Operations Center, Raven Slingshot, Trimble GFX-750) and manages secure, wireless data transfer to in-cab displays or cloud platforms, eliminating USB drives and manual data entry errors that delay field operations.

A LangGraph or CrewAI-based orchestration layer coordinates specialized agents for detection validation, rule application, and quality assurance. One agent cross-references detections with recent weather and soil sensor data to filter false positives. Another routes low-confidence zones for human agronomist review via a dashboard queue before final map generation, ensuring operational trust and preventing costly misapplications.

A critical governance component that automatically creates an immutable record for every prescription. It logs the source detection imagery, the applied agronomic rules, the final prescription file, and—after integration with as-applied data—the actual execution. This builds a defensible audit trail for regulatory compliance (e.g., pesticide reporting, nutrient management plans), organic certification, and sustainability reporting (ESG).

This integration agent ensures the workflow operates within the farm's system of record. It pulls field boundaries and input inventory levels from platforms like SAP Agri, Proagrica, or Cropwise. After map generation, it pushes the prescription details back to the ERP to decrement inventory and create a work order, closing the loop between scouting, planning, and execution while maintaining accurate financial and operational data.

The final component in the automation cycle schedules follow-up drone flights post-application. Agents compare pre- and post-treatment imagery to quantify efficacy. Results are fed back to the rules engine to refine future prescriptions and, if model performance degrades, automatically trigger MLOps pipelines for model retraining using the new, labeled data, creating a self-improving system.