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

植物病原体是全球农作物生产的主要威胁，农​​作物病害的发病率不断增加，全球气候变化和农业扩大了病原体的地理范围，进一步威胁粮食供应。监测植物病原体的影响涉及通过对细胞死亡的严重程度进行评分或测量植物上病斑的大小来评估免疫反应或疾病。目前的评分方法基于样品照片（使用可见光和/或紫外线）、图像处理（例如使用 Photoshop）、布局以及通过与外部参考图像进行比较进行手动评分，这是许多重要实验的严重瓶颈。为了应对植物病原体的威胁，我们需要能够在实验室和现场进行快速、高通量细胞死亡/病变评估的工具。整个项目的目标是开发一种新颖的机器视觉工具，使用深度学习模型（例如可以自动对图像进行评分的卷积神经网络）。

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.