Enhancements to Smart Disease and Pest Prediction System Through the Use of Machine Learning Techniques

Given the current global environmental crisis, developing sustainable solutions to enhance or replace our current agricultural practices is critical: the agricultural sector exerts important environmental pressure through its aggressive land, water and pesticide usage combined with the ever increasing demand on food supply. Mitigating this problem requires developing more sustainable and efficient agricultural techniques. Precisely, advances in networking and sensing technologies allows us to gather vast amounts of data in the fields that can be fed to complex machine learning and data mining algorithms to enhance current pest and disease prediction models. Such models feature unprecedented accuracy and flexibility, ultimately allowing farmers to significantly reduce their water and pesticide usage. This is exactly the kind of solution the current project will focus on by helping Ukko Agro, a Canadian company, extend the capabilities of their current precision agriculture solution.

Intern: 
Antoine Viscardi
Faculty Supervisor: 
Scott Sanner
Province: 
Ontario
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