Developing Deep Learning Models And Tools To Score Plant Cell Death And Disease Lesion Severity

Plant pathogens are a major threat to global crop production, incidence of crop disease is
increasing, and global climate change and agriculture expands the geographical range of
pathogens threatening food supply further. Monitoring effects of plant pathogens involves
assessment of immune responses or disease by scoring severity of cell death or measuring
size of disease lesions on the plant. Current scoring methods are based on photos of samples
(with visible and/or UV light), image processing (e.g with Photoshop), layout and manual
scoring by comparison with an external reference image, a severe bottleneck to many
important experiments. To deal with the threat of plant pathogens we need tools that can
perform fast, high-throughput cell death/lesion assessment in the lab and field. The overall
project aim is to develop a novel machine vision tool using deep learning models like
Convolutional Neural Networks that can score images automatically.