AgriTech AI: Smart Quality Control

Manual quality inspection in agriculture is time-consuming and prone to error. The challenge was to develop an automated system that uses computer vision to track quality trends, detect defects in real-time, and provide actionable analytics for improved decision-making.

As a Product Designer, I led the design process entirely—from initial user research and wireframing to high-fidelity prototyping and developer handoff. My focus was on creating a scalable design system and ensuring a seamless user experience.

To understand user needs, I conducted competitive analysis and user interviews. Key insights revealed that users prioritized speed, clarity, and trust. These findings guided the information architecture and visual hierarchy of the final product.

The design process involved multiple iterations. Starting with low-fidelity sketches to validate flows, I moved to mid-fidelity wireframes for layout testing. Visual design decisions were driven by accessibility standards and brand identity.