AI Agentic Workflow for Predicting Field-Level Yield Variance 60 Days Pre-Harvest

This agentic workflow targets the critical operational bottleneck of harvest uncertainty, where last-minute logistics scrambles and suboptimal marketing decisions erode margin. By ingesting late-vegetative drone data—canopy cover, plant height, NDVI—and fusing it with hyper-local weather forecasts, it generates a 10m-grid yield variance model 60 days pre-harvest. The architecture replaces guesswork with a quantified forecast, enabling precise allocation of labor, drying capacity, and grain storage, while providing a proprietary edge for forward contracting based on field-level potential.

Implementation requires integrating with platforms like John Deere Operations Center or farm ERP systems (SAP Agri) for historical context. The orchestrator, built on LangGraph, manages agent tasks, data validation, and exception routing. Key controls include confidence scoring on predictions, mandatory human review for low-confidence zones, and audit trails linking forecasts to final yield data for continuous model retraining. This turns speculative planning into a governed, automated operational process.

This workflow automates a critical operational bottleneck: the manual, error-prone process of translating late-season field data into a reliable harvest forecast. By ingesting drone-derived canopy cover, plant height, and health scores alongside hyper-local weather forecasts, it models yield potential and variance at a 10m x 10m grid level. The operational upside comes from improved harvest logistics planning, optimized grain bin allocation, and more confident forward marketing, directly protecting margin and reducing costly operational surprises.

Implementation requires integrating with farm management platforms (e.g., John Deere Operations Center) for boundary data and historical context. The orchestration engine, built on frameworks like LangGraph, manages agentic workflows for data validation, model inference, and exception routing. Critical controls include confidence scoring for predictions, automated alerts for data drift, and mandatory human review gates for forecasts falling outside expected variance bounds, ensuring operational trust and auditability.

Phase 1 establishes a single-field pilot, ingesting drone-derived canopy cover, plant height, and health scores via a cloud data lake. A lightweight orchestration layer, built with LangGraph, runs a baseline yield model against historical data and weather forecasts, outputting a 10m-grid variance map to a dashboard. This 4-week sprint validates data pipelines, model performance against ground truth, and defines key exception types for human review, proving the core operational and accuracy uplift before investment in scale.

Phase 2 scales the orchestrator to manage hundreds of fields, integrating with the farm ERP (e.g., SAP Agri) for boundary and historical yield data. Phase 3 hardens production with approval gates for high-variance predictions, SMS alerts to managers, and full observability for model drift. Each phase delivers measurable ROI: the pilot reduces manual yield estimation by 70%, scaling cuts weekly analysis from days to hours, and production controls prevent costly misallocation of harvest logistics and marketing contracts.

Operationalizing a 60-day pre-harvest yield model demands controls that manage data quality, model confidence, and exception routing. The architecture must validate incoming drone metrics and weather forecasts, score prediction confidence per 10m grid, and route low-confidence zones to agronomist review before forecasts influence logistics or marketing contracts. This prevents costly misallocation of harvest resources and maintains trust in the AI's recommendations, turning raw predictions into a managed operational signal.

A phased rollout mitigates risk. Phase 1 runs the model in shadow mode, comparing predictions to historical yields without triggering actions. Phase 2 introduces human-in-the-loop approvals for a subset of fields, refining alert thresholds and review interfaces. Phase 3 enables automated data publishing to the FMIS (e.g., John Deere Operations Center) for high-confidence zones only, with full audit trails linking predictions to final yield data for continuous model retraining and performance validation.