Code Reading

Sorting of tobacco variants in the production unit has been a major setback for our client because of human error and limited accuracy. And this results in lesser production profitability. The removal of manual segregation of tobacco and an automated solution was the primary concern.

Deep Neural Network helps in optimal decision-making with high accurate results for image processing. Processing of images without DNN operates by comparing the captured image to its master image. Since there is no robust and accurate algorithm, the classification of variants becomes complex for rule-based image processing solutions. But with Deep learning’s best algorithm this complex classification can be achieved. An area scan camera is used with an appropriate Field Of Vision (FOV), so that the images taken are sharp and clear.

To know more about our solution,
Please read our case study
Tobacco Classification
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