We will show how 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 chip/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 chip/crisp processing due to the Maillard reaction.
In this webinar series, Aris Karanikas (Business Development Officer) and Vincenzo Lagani (VP of Bioinformatics) at JADBio will demonstrate the advanced capabilities of AutoML to assist researchers and agronomists in data analysis. They will explain how to apply the JADBio platform based on real-life agricultural case-studies. Artificial intelligence (AI) and application of machine learning models are currently trending in the agriculture industry, and you will learn how it can help you to make better analytic decisions and improve your data interpretation efficiency.
By attending this webinar, you will discover:
- How you can analyze and classify your potato samples, without extensive data science knowledge
- Discover which specific features play a role in high quality potatoes, along with their relative strength as predictors
- Understand how relevant sets of equivalent predictors can also affect the desired result
- How to apply your model on all future potato samples
- How AutoML can help the agriculture industry in more efficient seed production, breeding, and many other sectors of the industry
Who is this Webinar for:
and anyone who needs to discover how they can utilize machine learning to predict crop performance, without the need to learn data science or acquire programming skills.
All attendees will receive a fully functional monthly licence (free of charge) for JADBio AutoML