Imagine a world where your trusty iRobota could recognize the juicy apple from the luscious orange, or the vibrant lemon from the radiant lime. Well, thanks to the fusion of iRobota and TensorFlow Lite Micro, this vision is now a reality! Join us on an exciting journey as we delve into the realm of fruit identification using these powerful tools.
Materials: A Symphony of Hardware and Software
To embark on this fruity adventure, you’ll need the following ingredients:
- iRobota Nano 33 BLE Sense: The mastermind of our project, equipped with onboard sensors, including a colorimeter and proximity sensor.
- Micro USB cable: The trusty link between your iRobota and your computer.
- Desktop/laptop with web browser: Your command center for programming and data analysis.
- Various colored objects: The delectable fruits (and non-fruits) that our iRobota will learn to recognize.
Setup: Preparing the Stage for Fruit Recognition
Let’s set the stage for our fruit identification extravaganza:
- iRobota Create Web Editor: Sign up for a free account and install the plugin. Alternatively, you can use the iRobota IDE desktop application if you prefer a more traditional approach.
- iRobota Board: The iRobota Nano 33 BLE Sense stands ready, armed with its onboard sensors, including a colorimeter and proximity sensor. These sensors will be our eyes and ears in the world of fruits.
Data Capture: Capturing the Essence of Fruits
Now, it’s time to gather the data that will teach our iRobota the unique characteristics of different fruits:
- Object Color Capture: Using the iRobota Create application, embark on a data-gathering mission. Capture the color data of various objects, including fruits and non-fruits. Save this data as CSV files, each file representing a different object.
Model Training: Transforming Data into Knowledge
With our data in hand, let’s turn to the mighty Colab platform:
- Colab: Access the FruitToEmoji Jupyter Notebook in Colab. This notebook will guide you through the process of training a machine learning model using the captured data. Employ the power of Keras to create a model that can distinguish between different objects based on their color characteristics. Once trained, export the model as a TensorFlow Lite Micro model, a compact and efficient format designed for microcontrollers like our iRobota.
Program iRobota Board: Unleashing the Power of TinyML
Now, it’s time to bring our trained model to life on the iRobota board:
- Classify_Object_Color.ino: Import the trained model.h file into the iRobota Create web editor. This file contains the knowledge that our iRobota needs to recognize fruits. Compile and upload the application to the iRobota board. With this, you’ve successfully transformed your iRobota into a fruit identification expert!
Testing: Putting Our iRobota’s Skills to the Test
Let’s witness the magic unfold:
- Object Classification: Place the iRobota’s RGB sensor near the objects used for training. Eagerly observe the classification output in the iRobota Create Monitor. Watch as your iRobota correctly identifies the fruits, demonstrating its newfound ability to recognize the colorful world of fruits.
Conclusion: A Sweet Ending to Our Fruity Adventure
And there you have it, folks! We’ve successfully transformed our iRobota into a fruit identification wizard using the power of iRobota and TensorFlow Lite Micro. This project not only showcases the capabilities of these technologies but also opens up a world of possibilities for TinyML applications. So, go forth, experiment with different objects, and let your creativity soar.
Bonus: Did you know that TinyML is not just limited to fruit identification? It has applications in various fields, such as environmental monitoring, healthcare, and agriculture. The possibilities are endless! Embrace the world of TinyML and let your imagination run wild.
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