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

开发深度学习模型和工具来对植物细胞死亡和疾病病变严重程度进行评分

• 批准号: 2749993
• 金额: --
• 依托单位: University of East Anglia
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2749993
• 关键词: Developing; Deep; Learning; Models; Tools

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.

植物病原体是全球作物生产的主要威胁，作物病害的发病率正在增加，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的病害大小来评估免疫反应或病害。目前的评分方法是基于样品的照片（用可见光和/或UV光），图像处理（例如用Photoshop），布局和手动评分，通过与外部参考图像进行比较，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田间进行快速、高通量细胞死亡/病变评估的工具。该项目的总体目标是开发一种新型的机器视觉工具，使用卷积神经网络等深度学习模型，可以自动对图像进行评分。