AgriTech
Enabling Real-Time Monitoring for Farm Seeding Machines
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CONTEXT
Designing an MVP dashboard to give technicians and engineers real-time visibility into machine health and performance for faster action and improved customer experience.
Vaderstad is a Swedish company that specializes in agricultural machinery, particularly in the design and manufacturing of soil preparation and seeding equipment for farming. They aimed to transform the seeding (FeedSeed) machines into digital twins—virtual replicas connected via IoT sensors to monitor performance in real time, detect issues, and optimize machine utilization.
Client
My Responsibilities
Dashboard Design, Visual Design, Prototyping, Information Architecture, Design System, Product Strategy
Tools
Adobe XD, Miro
Team
2 Designers
Timeline
4 Weeks
IMPACT
The MVP (Minimum Viable Product) successfully
secured client buy-in to expand this into a full-scale project.
CHALLENGE
Translating complex IoT data into actionable, user-friendly visualization.
The real challenge was structuring and prioritizing machine data in a way that allowed users to access what they needed quickly, while also surfacing system-level insights like patterns of recurring alarms across geographies.
OUTCOME
Usecase 01
The dashboard map view was designed to provide a real-time fleet overview, displaying all feedseed machines in operation with status indicators.
Machines operating normally were displayed in their default state, while those with active alarms were visually highlighted with alert icons
Usecase 02
The asset list offered a sortable overview of all machines.
Machines with the highest severity were automatically prioritized at the top, helping teams quickly identify and respond to the most critical issues. This enabled technicians and engineers to triage effectively.
Here is how we reached there…
The highlighted areas show my responsibilities.
CONTEXT
LEARNINGS AND TAKEAWAYS
Being new to the concept of digital twin, understanding the domain in context of sensor rich agricultural machinery required a steep learning curve.
Collaborating closely with IoT engineers helped me better interpret how raw sensor data translates into machine behavior and operational states.
Not all data is equal—deciding what to show, when, and to whom made the difference between an overwhelming UI and a usable dashboard.
Due to the NDA nature of the project, feel free to reach out for more information or call me for an interview :)
