AgriTech AI: Smart Quality Control

Computer Vision for Agriculture

AI-Powered Agriculture Quality Control System with real-time defect detection, live monitoring, and actionable analytics.

Final screens

3 screens · click to expand

Problem & context

The challenge

Manual quality inspection in agriculture is slow, inconsistent, and labor-intensive. Defects missed at inspection reached consumers — damaging brand trust and creating financial waste.

Role & responsibilities

What I owned

Designed the full AI dashboard product experience — from live camera feed UI, defect classification display, analytics views, to the summary reporting screen for daily quality oversight.

Product DesignDashboard UXData VisualizationEdge Computing UX

Research & insights

What I found

Field visits to 2 packing facilities revealed inspectors were managing multiple conveyor lines simultaneously

Cognitive overload was the root problem — they couldn't watch everything

The system needed to notify, not demand constant attention

Outcome & impact

The solution

Designed an AI-driven dashboard with real-time defect flagging, sound alerts for high-severity items, and clean analytics charts for trend tracking. Deployed on Raspberry Pi for on-site edge processing.

Real-time Defect Detection

Reduced Manual Effort

Detailed Analytics

Optimised Quality Control

More work