Peanut Pest & Disease Detection Platform
Project Year: 2020
A senior-year capstone project focused on peanut pest and disease recognition. The project combined a self-built motorized scanning machine, edge AI inference, and a custom Android controller, and received the department's Honorable Mention award.
Tech Stack
Architecture
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- Mechanical layer: aluminum extrusion frame, rope-driven movement path, and 3D-printed custom parts.
- Control layer: Raspberry Pi coordinates workflow, while Arduino handles low-level motor actuation.
- Vision layer: three cameras capture crop images in each scan cycle.
- Inference layer: YOLO model runs with Neural Compute Stick acceleration for edge-side prediction.
- Interaction layer: Android app issues movement and capture commands to the machine controller.
Features
- Self-built motorized scanning machine using aluminum extrusion and custom 3D-printed parts.
- Hybrid control architecture with Raspberry Pi as orchestrator and Arduino as motor controller.
- USB-based communication between control layers for predictable hardware coordination.
- Edge inference pipeline accelerated by Neural Compute Stick for near real-time detection.
- Custom Android app for remote movement control and capture process handling.
- YOLO model fine-tuned with peanut disease and pest data for domain-specific recognition.
- Triple-camera capture workflow in each run to improve coverage and detection reliability.
Outcomes
- Built a complete end-to-end prototype from mechanical design to AI inference and mobile control.
- Validated a low-cost edge-AI workflow for peanut pest and disease screening.
- Received departmental Honorable Mention in the senior-year capstone evaluation.
Demo Screens
Roadmap
- Design the 3D machine architecture
01
Defined the full frame layout, camera placement, and movement strategy before hardware implementation.
01 - Assemble the machine using aluminum extrusion and 3D-printed parts
02
Purchased aluminum profiles for the main frame and 3D printed custom connectors and mounting components.
02 - Build the control system with Raspberry Pi + Arduino via USB
03
Used Raspberry Pi as the main controller and Arduino as the motor driver unit with USB serial communication.
03 - Integrate Neural Compute Stick for edge real-time inference
04
Deployed on-device inference to reduce latency and enable practical field-side detection.
04 - Develop Android app for machine movement control
05
Implemented an Android app to control motion paths and trigger capture workflows.
05
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