The challenge
Pineapple ripeness is judged by eye, which causes waste across the supply chain.
What we built
An electronic nose built on Raspberry Pi with a gas-sensor array and an ML classifier that reads ripeness from fruit emissions.
01
Sensor array designSelected and calibrated gas sensors on a Raspberry Pi rig.
02
Dataset & featuresCollected emission readings across ripeness stages and engineered features.
03
Model & deviceCompared classifiers, then deployed the best model on-device.
[ SCREENSHOT · MAIN VIEW ]
[ SCREENSHOT · DETAIL ]
87%RIPENESS DETECTION ACCURACY
87% accuracy classifying pineapple ripeness from gas emissions using low-cost hardware.
Client quote goes here: one or two sentences on the result and the working relationship.
Raspberry PiGas sensorsPythonscikit-learn