PhD Reducing potato losses by creating a predictive model for black dot disease

This PhD project will test whether in field environmental monitoring, digital plant phenotyping and improved postharvest management can be combined to predict black dot disease to avoid losing pre-pack quality during cold storage. The work aims to use sophisticated photonics and associated algorithms, machine learning and data integration methods across the pre and postharvest continuum to create a predictive model for black dot incidence and severity during storage and evaluate how resilience can be improved in response to different climate change scenarios. The model would predict when best to market a crop in cold storage and thereby reduce food loss and waste.

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