SmartGrader IoT — Agricultural Produce Grading Platform

SmartGrader IoT - Use Case

Designed an AI + IoT-enabled platform for grading palm oil fruits in-field. SmartGrader uses vision sensors and embedded machine learning to assess quality, automating manual grading and improving accuracy for farmers and cooperatives

Approach

• Conducted field research with 20+ farmers and aggregators to map current workflows.

• Collaborated with IoT engineers to understand device constraints (camera, sensors, latency).

• Designed the device UI and mobile companion app for real-time grading feedback.

• Built prototypes in Figma; validated with 3 farmer cooperatives via pilot tests.

• Iterated design for on-field readability, multilingual support, and error recovery.

Solution Highlights

• IoT Edge Device: integrated vision sensor classifies produce by color, size, and defect parameters.

• Real-time Dashboard: mobile app syncs via Bluetooth/Wi-Fi for live grading results and lot summaries.

• Multilingual Interface: supports 3 local languages with voice-guided prompts for semi-literate users.

• Data Cloud Sync: centralized storage enables analytics and buyer-grade reports.

• Visual Feedback Loop: intuitive LED and sound indicators reduce user learning curve.

Challenge

Traditional produce grading in Indonesia relied heavily on manual inspection — subjective, slow, and inconsistent across operators. Farmers often faced rejections from buyers due to human errors and delays, affecting income and trust in the supply chain.

Results & Impact

Pilot deployment showed ~30% reduction in grading errors and doubled throughput during peak harvest. Farmers reported increased trust in fair pricing and reduced disputes with aggregators. SmartGrader’s scalable UX enabled integration across multiple produce types (Coconut and other trees).