基于水稻表型信息的白叶枯病快速诊断机理和方法研究

水稻白叶枯病是水稻三大病害之一，对水稻的产量和品质有较大的影响。本项目采用表面增强拉曼光谱、单细胞分析、红外热成像、高光谱成像等信息获取技术结合数据处理算法，建立不同基因型水稻白叶枯病耐受性表型的定性与定量分析模型，形成系统科学的评估方法体系。通过研究不同基因型水稻在不同尺度（细胞、器官、植株、群体）、不同生长时期和不同环境（实验室、大田）下表型的差异，帮助育种专家加速水稻白叶枯病耐受性品种的育种进程。结合人工智能算法以数字化手段形成评估模型，为水稻白叶枯病的早期检测提供新的思路和方法。通过分析黄单胞杆菌在水稻不同组织中的侵染情况以及病害胁迫下不同基因型水稻的分子响应与相关表型的相互关系来解析水稻的抗病机理。最终建立起对水稻白叶枯病耐受性表型的快速无损检测体系，为作物生长和育种实验提供支撑，具有十分重要的意义。

Bacterial blight is one of the three major diseases of rice, which has a great influence on the yield and quality of rice. In this project, different detection methods such as surface enhanced Raman spectroscopy, single cell analysis, infrared thermal imaging and hyperspectral imaging techniques and data processing algorithms were used to establish qualitative and quantitative analysis models of rice bacterial blight resistance phenotype, forming a systematic and scientific evaluation method system. By studying the phenotypic differences of different genotypes of rice at different scales (cells, organs, plants, populations), different growth periods and different environments (laboratory and field), it helps breeding experts to speed up the breeding process of rice bacterial blight resistance varieties. Combining with artificial intelligence algorithm to form evaluation model by digital means is helpful to provide new ideas for early detection of rice bacterial blight. The disease resistance mechanism of rice will be analyzed through the analysis of Xanthomonas oryzae pv. Oryezae infection in different tissues of rice and the relationship between molecular response and related phenotypes of different genotypes of rice under disease stress. Therefore, it is of great significance to establish a rapid non-destructive detection system for the tolerance phenotype of rice bacterial blight, which can provide support for crop growth and breeding experiments.