2Nong – Digital Farming Assistant

Research & Insights

We conducted user interviews and field observations with farmers.

User Personas

Mr. Tu

60 years old

Rural • Low tech familiarity

Relies on experience and visual observation to identify crop issues. Has difficulty with digital tools and prefers simple, clearly guided interactions.

• Learns through visuals
• Needs quick answers
• Uses mobile apps infrequently

"I just want to know what disease the plant has and how to treat it."

Ms. Ba

32 years old

Active user • Learns from community

Actively joins farming communities to ask questions and share knowledge. Values trust and validation from fellow farmers.

• Learns through community
• Trusts advice from peers
• Wants to make better decisions

"I trust what other farmers say more than generic advice."

Mr. Bay

48 years old

Tech-savvy • Data-driven

Uses data and tools to optimize farming productivity. Cares about market trends, pricing, and scalable insights.

• Needs market information
• Uses technology confidently
• Focuses on efficiency

"I need reliable data to make the right decisions."

These personas shaped key design decisions toward a visual-first, guided experience that reduces complexity and builds user trust.

• Farmers rely primarily on visual recognition, not text
• Trust increases through clear actions
• Users prefer guided flows
• Too many choices → abandonment

Solution

Based on user behavior and needs, we designed a guided, low-barrier experience that helps farmers quickly identify crop problems and take immediate action:

• Capture a crop photo
• AI processing
• Instant diagnosis
• Suggested treatment methods

Design Approach

We translated user insights into a design approach that simplifies complexity, reduces cognitive load, and supports product scalability:

• Simplify AI analysis into understandable information
• Reduce cognitive barriers
• Build a scalable system

Design System

We developed a flexible design system that connects design and development, ensuring consistency, reducing implementation gaps, and accelerating product updates across both App and Web platforms.

Transformation Direction

The final UI transformed complex agricultural data and AI outputs into clear, human-centered experiences, bridging the gap between technology and practical use.

Onboarding & Login

The onboarding experience uses familiar agricultural imagery to quickly create understanding and trust, reducing barriers for new users.

The login flow prioritizes speed and convenience through phone number authentication, a familiar method for users that reduces data entry and enables quick access.

AI Crop Disease Diagnosis

The AI Crop Disease Diagnosis feature helps farmers quickly detect crop diseases through a guided photo capture flow, ensuring accurate and reliable input.

The system translates complex AI analysis into clear, actionable information, including disease identification, visual highlighting of affected areas, and step-by-step treatment recommendations so users can act with confidence.

Market Insights

The Market Insights feature turns complex pricing data into clear, easy-to-understand information, helping farmers quickly understand market trends.

With region-adjusted pricing and visual indicators, users can make timely and accurate decisions for buying, selling, and planning farming activities.

Impact

The final product delivered measurable improvements in speed, accuracy, and user confidence, helping farmers make faster and more informed decisions.

40%

Faster diagnosis time

60%

Improved early detection

80%

Task completion under 30 seconds

Reflection

This project highlights an important shift: success is not defined by how intelligent the system is, but by how clearly users can understand and act on it.

For farmers, effective design means:

• Clarity over complexity
• Guidance over information overload

Next Steps

Looking ahead, the focus is on expanding the ecosystem and improving accessibility by addressing essential user needs and product gaps:

• Offline mode for rural areas
• Voice interaction to support older users
• Community knowledge sharing
• AI confidence visualization by highlighting affected crop areas