Using neural networks to detect ripeness in fruit and vegetables

As the industry wants to automate as much of the food production process as possible, it becomes necessary to have machines sort the ripe and ready produce from the rest at times. Turkish innovator Kutluhan Aktar claims he has found a way to do just that, using the power of neural networks.

His project’s goal is straightforward; it aims to detect ripeness in fruit and vegetables by monitoring pigment changes. 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.



Source: Fresh Plaza