Machine learning tomato classification using Particle Argon #ColorSensing #FeatherFriday @Particke

ML for Classification of Tomatoes Using Particle Argon

Team fablabz is classifying tomatoes into Grade A, Grade B and Grade C with TensorFlow Lite and Particle Argon.

Tomato is one of the most popular and widely grown vegetable crops in the world. Color in tomato is the most important external characteristic to assess ripeness and post harvest life. Degree of ripening is usually estimated by color charts. There are six ripening stages reflecting human ability to differentiate ripeness: green, 100% green; breaker, a noticeable break in color with lesser than 10% of other than green color; turning, between 10 and 30% of surface, in the aggregate, of red(ish) color; pink, between 30 and 60% of red(ish) color; light red, between 60 and 90% and red, more than 90% red. We have taken 3 stages and classified them into grades.

The project used Particle Argon for computations and Adafruit TCS34725 sensor to capture the colors.

Read how they trained the Tensorflow Lite model to classify tomatoes and more in the article on

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