During a webinar a team of researchers at JADBio will demonstrate how they applied the company’s Automated Machine Learning (AutoML) solution to quickly analyze a complex set of data during a recent project with different potato varieties. The researchers were able to successfully predict potatoes’ susceptibility to bruising as well as the potato samples’ potential for coloration during chip/crisp processing.
The webinar will be presented by experts at JADBio in partnership with Potato News Today. It is titled: “How to employ Automated Machine Learning to Predict the Best Quality Potato Chip/Crisp“, and will be hosted on Thursday, June 24 via Zoom.
“Our team wanted to differentiate between potatoes that would be less prone to bruising from those that would more easily bruise during mechanical handling,” says Aris Karanikas, Business Development Officer at JADBio. “And we also wanted to predict the potatoes’ potential susceptibility to acrylamide formation during chip/crisp processing due to the Maillard reaction,” he says.
Vincenzo Lagani, JADBio’s VP of Bioinformatics explains that the research group gathered relevant data from a total of 478 different potato samples, including information on climate and soil where the potatoes were grown in Germany, as well as the metabolic profiles of the tubers. “We were then able to analyze the data and build a predictive model with JADBio’s AutoML platform in only a few minutes,” he says. “We succeeded in successfully producing an executable model with numerous performance metrics related to bruise susceptibility and browning parameters.”
In this webinar series, Aris Karanikas and Vincenzo Lagani will demonstrate the advanced capabilities of AutoML to assist researchers and agronomists in data analysis. They will explain to attendees how to apply the JADBio platform based on real-life agricultural case-studies.
“Artificial intelligence (AI) and the practical application of machine learning are currently trending in the agriculture industry,” says Karanikas. “During this webinar attendees will learn how our AI solution can help them to make better analytic decisions and improve on their data interpretation efficiency.”
Anyone who wishes to discover how they can utilize machine learning to predict crop performance, without the need to learn data science or acquire programming skills, is encouraged to register for the webinar. Researchers, agronomists, staff at processing facilities, storage managers and other potato professionals are cordially invited to attend.
All attendees will receive a fully functional monthly license (free of charge) for the JADBio AutoML platform that they can then use to create their first AutoML model with their own data, or with the datasets available within the platform, including the “Predicting Optimal Potato Crisp” dataset.
Note: A recording of the webinar is now available for viewing. Visit this page to watch and download related data.