A Turkish researcher has developed technology that uses data from a visible light sensor to identify the ripeness of produce.

The project’s goal is to detect ripeness in fruit and vegetables by monitoring pigment changes, Hack A Day reports. Rather than use a camera, the project relies on data from an AS7341 visible light sensor, which is better suited to capturing accurate spectral data.

This allows a better read of the actual light reflected by the fruit, as determined by the pigments in the skin which are directly related to ripeness.

Sample readings were taken from a series of fruit and vegetables over a period of several days, which allowed a database to be built up of the produce at various stages of ripeness.

This was then used to create a TensorFlow model which can determine the ripeness of fruit held under the sensor with a reasonable degree of certainty.

The build is a great example of the use of advanced sensing in combination with neural network - a subset of machine learning that are at the heart of deep learning algorithms. 

It is unclear how much more accurate the results are compared to using a camera.