Sherpa NIA

NDA | Smart agriculture | Mobile UX | React Native | AI-assisted decision support

Turning real-time farm data and AI recommendations into guidance farmers can trust.

  • Role: UX/UI Designer and Front-End Developer
  • Platform: Mobile application
  • Public visibility: Confidential. Use generalized language and public visuals.
  • Scope: Research synthesis, UX/UI design, React Native implementation, API/MQTT integration, data visualization, accessibility and localization-ready architecture.

  • Introdution

    I designed and built a confidential mobile platform for smart agriculture, focused on helping farmers monitor environmental conditions, understand crop health signals, and act on AI-assisted recommendations. The work centered on trust: users needed to understand not only what the system detected, but what action they could take next.

  • The challenge

    The app had to work in real farming contexts where connectivity, device quality, lighting conditions, and technical familiarity could not be assumed. Data alone was not enough; the interface needed to translate sensor readings, alerts, and machine-generated outputs into clear, practical guidance.

  • My contribution

    • Analyzed field research materials and farmer workflows to define product requirements.
    • Designed mobile flows for farm setup, monitoring, AI-assisted diagnosis, alerts, recommendations, history, and offline-aware behavior.
    • Built the front end in React Native and integrated real-time data updates and API-based features.
    • Created visual patterns that paired data values with plain-language states, icons, and guidance to support comprehension in outdoor conditions.
    • Collaborated with engineers, agronomy researchers, and AI/data specialists to keep the experience scientifically and technically grounded.

  • Outcome

    The resulting product direction expanded the company’s smart agriculture work from monitoring into decision support. The project strengthened my approach to human-centered AI: design must make uncertainty, confidence, and recommended action understandable, especially when users are making real-world decisions.

  • Designing for trust in the field

    This work reflects how I design for real-world conditions: imperfect connectivity, environmental uncertainty, varied technical familiarity, and decisions that depend on trustworthy data. It also highlights my ability to collaborate across design, engineering, AI, and agricultural science without losing sight of what farmers need to understand and do next.