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

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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.