The Virtual Quality Assistant aims to automate in-line quality measurements. It uses AI models to measure quality characteristics such as dimensions, color, number of defects, type of defects, and dry matter content, all in-line. The results are then used to optimize process parameters or to visualize insights on a dashboard.
Thanks to advanced models, we can also automatically measure the number and type of defects in-line. These AI models are trained using "synthetic" or simulated data, allowing for quick and scalable deployment. With these models, potatoes can be measured even when they are stacked in multiple layers. This ensures that the process itself does not need to be adjusted for the implementation of automated quality control.
The Virtual Quality Assistant also includes a dashboard for visualizing the results. Additionally, reports can be generated to document the quality of a specific batch. Within a production process, the results can also be linked to the optimization of process parameters.
By reducing the feedback loop between detecting a quality deviation and adjusting the process parameters, a significant reduction in waste is achieved. Additionally, the variation in potatoes will be measured and acted upon. This leads to more stable quality as well as higher overall quality.
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Agristo Virtual Quality Assistant - Interpom
