VineOps
I designed the operator experience for an autonomous agricultural rover platform — translating field data from onboard cameras into actionable insights for vineyard managers in California. End-to-end UX across iPad and mobile, working across hardware, software, and ISO compliance teams.
2024
Year
14 Mo [pt]
Duration
AgriTech + AI
Category
Figma | Figjam | Miro
Stack
Problem
A leading agricultural equipment manufacturer was launching a new autonomous rover designed to scout vineyards in California, collecting high-resolution imagery and field data via onboard cameras. The hardware was advanced, but the software layer was missing: vineyard managers had no way to translate raw scouting data into operational decisions like where to harvest, where to spray, or where disease was emerging. The challenge was to design an operator experience that worked across stakeholders, farm managers planning at the macro level, and crew members executing jobs in the field, while respecting strict ISO requirements for outdoor, high-glare, regulated agricultural equipment. It also had to coordinate with the limits of the rover hardware itself: an autonomous system whose behavior the user needed to oversee, not micromanage. Project under NDA — product name and visuals anonymized.
Solution
I designed an end-to-end platform across two surfaces: a IOS iPad application for farm managers, and a companion mobile app for crew members in the field. Farm managers could map their vineyards, monitor rover location and weather conditions, and program autonomous jobs; vehicle scouting jobs run by the rover, or crew jobs assigned to people. After scouting, the system surfaced actionable insights: heat maps of grape ripeness, disease detection, and recommended next jobs (harvest, spraying, pruning) based on the data collected. The mobile app translated those decisions into clear, low-friction tasks for crew members on the ground: where to go, what to do, and how to log completion, designed for outdoor conditions and minimal training overhead. The system was designed around a core interaction principle: the AI is the primary actor, the UI supports oversight. Operators set intent; the rover executes; the interface surfaces what needs human judgment.
Impact
Ownership of UX from research to launch
Coverage across iPad and mobile surfaces
Core flows end-to-end
Tested with real vineyard operators in California
System architecture
Manager decision flow
AI-as-actor, UI-as-oversight roles
Crew app flow
Key Learnings
Designing for autonomous systems is a different discipline. The interaction model isn't "user gives command, system executes", it's "user sets intent, system acts, user oversees." Most of the design work was about building trust in the system's autonomous behavior without burying the user in detail.
Cross-disciplinary constraints sharpen design decisions. ISO compliance, outdoor visibility requirements, and hardware limitations narrowed the design space, but the constraints made the final product clearer, not weaker.
Field testing is the only ground truth in agritech. Lab usability findings rarely survived contact with real vineyards. The May testing round in California reshaped the harvest assignment flow significantly.
Hardware + software design is a coordination problem, not a design problem. The biggest challenge wasn't UI, it was syncing release timelines, firmware capability, and UX expectations across three teams.
Next Steps
Project under NDA, additional context available on request.
