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Determining ripeness using neural networks

14.08.2021

As the industry wants to automate the food production process as much as possible, from time to time it becomes necessary for machines to separate ripe and finished products from the rest. Turkish innovator Kutluhan Aktar has found a way to do this using the power of neural networks.

The purpose of this project is simple. It aims to determine the ripeness of fruits and vegetables by monitoring pigment changes. Instead of using a camera, the project uses data from the AS7341 visible light sensor, which is better suited for capturing accurate spectral information. This allows for a better understanding of the actual light reflected by the fruit, which is determined by skin pigments that are directly related to ripeness.

Samples were taken from fruits and vegetables over a period of several days, which made it possible to create a database of products at various stages of ripeness. All data was used to create a TensorFlow model that can determine the ripeness of fruits and vegetables held under the sensor.

The developed gadget is an excellent example of using advanced sensing in combination with neural networks. The researchers suspect that the results of his assessment are much more accurate than expected.

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