Role: Solo designer — end-to-end

Type: Course project (UX Architect, Ofer Monar Ph.D)

Project duration: 1.5 months

Scope: Discovery → IA → Flows → 4 screens

Methods: User interviews, task analysis, IA, competitive research, entity-based design

Users: Medium-to-large farm operators and managers

“FarmSight” is an innovative farm management system, tailored to farmers needs, aimed at streamlining operations, tracking crop performance, managing tasks and monitoring financial health for medium to large-sized farms.

The problem

Farm management is genuinely hard — not because farmers aren't sophisticated, but because existing tools aren't. Most solutions are fragmented, technically complex, or structured around the wrong priorities.

My assumption going in: farmers need better harvest tracking and field visibility.

What interviews revealed: The real priority is financial. Farmers run a business first, a farm second.

"Tracking expenses is the most important part for me to figure out" — Yael

"Go figure what you've done in block D32..." — Baruch

Six interviews later, the problem reframed itself: farmers needed one place to track what happened, what it cost, and what's ready to harvest — without needing a technical degree to use it.

How I think about unfamiliar domains

This was my first time designing for agriculture. I had strong process instincts but zero domain knowledge. That gap is actually a design problem of its own.

My approach:

1. Learn before you sketch. I read about farm operations and talked to farmers before touching Figma. Domain blindness produces interfaces that look right but behave wrong.

2. Let research reorder priorities. My assumption was that harvest tracking was the core need. Interviews showed expenses came first. I redesigned the information hierarchy around that — expense tracking became the anchor feature, not a secondary screen.

3. Use methodology deliberately, not decoratively. I used task analysis and IA not because the course required them, but because they were the fastest way to build a mental model of an unfamiliar role before designing for it.

Discovery process

Competitive research

I reviewed 7 farm management products — SeeTree, CropX, Regrow, and others. Most shared the same structural problems: modules designed in silos, data-heavy UIs with no clear hierarchy, and visual design that treated farmers as engineers.

The gap I found: no product made financial tracking and operational overview feel like the same job. They were always separate modules.

Role & task analysis

Before defining screens, I mapped what a farm manager actually does — not what I assumed they do. Task analysis gave me a structured view of frequency, criticality, and complexity across the role.

This became the basis for feature prioritization: high-frequency + high-criticality tasks got dedicated screens. Lower-frequency tasks got surfaced through progressive disclosure.

Information architecture

With tasks mapped, I built the IA to reflect how farmers actually think about their work — not how software typically organizes farm data. Each block represents a screen; the structure prioritizes daily operational tasks over administrative ones.

Main User Flows

Three flows, three jobs to be done.

The four screens — and the decisions behind them

Yearly planning (Gantt)

Farmers already used Excel for yearly planning. The insight wasn't that Excel is bad — it's that visualizing the same data differently creates value. An interactive Gantt by crop type, task type, and field block gives a spatial overview that a spreadsheet never can.

Key decision: progressive disclosure. The top level shows the annual picture; drilling down reveals field-level detail. This keeps the overview clean without hiding complexity.

An interactive visual planning Gantt chart for planning and tracking. Specifying crop types, task types, and blocks provides a comprehensive and intuitive overview of the entire farming schedule.

Task management

In large farms, task visibility is a coordination problem. Workers operate across multiple field blocks simultaneously, and managers lose track of what was done, by whom, and when.

The task dashboard answers one question clearly: what happened today, and what's still open? Assignment, deadline, and status in one view — no toggling between systems.

A dashboard with a summary of farm activities. By providing an area to assign tasks, set deadlines, and monitor progress, it enhances coordination among workers and optimizes resource allocation.

Harvest tracking

The screen answers four questions farmers ask constantly:

• What fields are ready to harvest now?
• What fields are coming up?
• What progress have we made?
• Which fields are performing best and worst?

Readiness indicators surface the answer before the farmer has to dig for it.

Harvest Tracking of every field and its’ data. Real-time monitoring and data-driven decisions enhance efficiency, while financial tracking and compliance reporting streamline management.

Expense tracking

This was the highest-priority screen based on research, so it got the most structural attention.

Actual vs. planned across five categories — crop production, labor, equipment, utilities, land — with enough granularity to spot where money is leaking, and enough simplicity that a non-financial person can read it without training.

"The field is the business, and the crops are the income" — Yael

What I learned

Assumptions are a design risk. I came in thinking harvest tracking was the core need. It wasn't. Research moved expenses to the top. If I'd skipped interviews, I'd have built the wrong product confidently.

Clarity first, interface last. The real work in this project wasn't designing screens — it was figuring out what farmers actually needed and in what order. The screens were the output of that thinking, not the thinking itself.

Methodology earns its place. Task analysis and IA aren't always the right tools. Here they were — because I needed to build domain knowledge fast and structure a complex role before I could design for it.

Use AI to populate, then edit with expertise. AI-generated content helped me fill placeholder data quickly so I could evaluate designs in realistic conditions rather than with Lorem Ipsum. The judgment about what data matters and how to display it still requires a designer.