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6 min read
Building a Smart Banana Ripeness Detector with TinyML and XIAO ESP32C3
TinyML
Edge Impulse
ESP32
Computer Vision
Machine Learning
The Journey
I created a smart banana ripeness detector using the Seeed Studio XIAO ESP32C3 and Edge Impulse's TinyML platform. This project brings machine learning to the edge, running real-time inference on a compact IoT device.
Technical Implementation
Hardware Setup
- Seeed Studio XIAO ESP32C3 with camera
- Battery HAT for portability
- MicroSD card for storage
- Built-in USB-C interface
Dataset Creation
- Captured banana images in different ripeness stages
- Labeled images following Edge Impulse guidelines
- Created balanced dataset categories
- Augmented data for better training
Model Development
- Edge Impulse online platform for training
- Optimized model for ESP32 constraints
- Transfer learning on proven architectures
- Model quantization for embedded deployment
Device Integration
- Firmware deployment to XIAO ESP32C3
- Real-time inference implementation
- Camera feed processing
- Web interface for results
Key Challenges & Solutions
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Model Optimization
- Balanced accuracy vs model size
- Optimized for limited memory
- Reduced inference time
- Maintained classification accuracy
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Image Processing
- Proper lighting considerations
- Consistent image capture
- Real-time frame processing
- Efficient memory usage
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Edge Deployment
- Successful model quantization
- Reliable inference pipeline
- Optimized power consumption
- Responsive web interface
Results & Learnings
The project successfully delivers:
- Real-time banana ripeness detection
- Edge-based inference
- Portable solution
- Web-based monitoring
This hands-on experience with TinyML demonstrated the power of bringing machine learning to embedded devices.
Future Enhancements
Exciting possibilities include:
- Multi-fruit classification
- Enhanced accuracy with larger dataset
- Battery optimization
- Mobile app integration
This project showcases how TinyML can turn simple IoT devices into smart classification systems. The intersection of embedded systems and machine learning opens up endless possibilities! 🚀