Across Regions, Equipment/Technology, Research, Webinars

Webinar recording now online: ‘How to employ Automated Machine Learning to Predict the Best Quality Potato Chip/Crisp’

This webinar ran successfully on June 24th. A video recording of the webinar is now available and can be watched here.

Aris Karanikas (Business Development Officer) and Vincenzo Lagani (VP of Bioinformatics) at JADBio demonstrated the advanced capabilities of AutoML to assist researchers and agronomists in data analysis. They explained how to apply the JADBio platform based on real-life agricultural case-studies.

A team of researchers applied JADBio’s Automated Machine Learning (AutoML) platform to predict potatoes’ susceptibility to bruising and also its potential for coloration during french fry / crisp processing. The aim was to differentiate between potatoes that would be less prone to bruising from those that would more easily bruise during mechanical handling. Another goal was to successfully predict the potatoes’ potential susceptibility to acrylamide formation during processing due to the Maillard reaction.

Researchers, agronomists and others working with potato research data were informed on how they can analyze and classify potato samples, without extensive data science knowledge, and learned which specific features play a role in high quality potatoes, along with their relative strength as quality predictors.

On this page of the JADBio website you can watch the video recording of the webinar. View the potato chip quality analysis here. View the potato blackspot bruising analysis here.

Special offer: Sign up here to use the JADBio platform for one month free.

Source: JADBio
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