马铃薯是我国第四主粮，高通量检测其植株表型对高品质育种、科学栽培与精细管理有重要意义。作为以羽状复叶和块茎收获为特征的作物，马铃薯植株表型快速检测还存在：植株羽状复叶形态提取与检测不够准确，羽状复叶叶片生理生化表型提取不够深入，生育进程形态-生理表型组检测不够系统等瓶颈问题，无法满足高品质育种、科学栽培与精准管理的需求。因此，本项目在已有研究基础上开展：①基于三维空间的马铃薯羽状复叶叶片空间形态特征提取与LAI高精度检测方法研究；②基于高光谱与封闭式荧光成像仪的日光诱导叶绿素荧光辐射微弱信号提取方法研究，建立基于高光谱的叶绿素含量与“光合指针”叶绿素荧光动力学参数检测模型，阐明叶绿素含量、光合速率与荧光参数关联，解析小叶与小裂叶光合能力差异；③基于多源多维度数据融合的羽状复叶形态-生理生化参数关联提取与生育进程动态检测方法研究。本项目成果将是构建马铃薯植株生育进程表型组检测体系的主要支撑。

The potato is the fourth major food in China. It is important to promote high quality breeding, scientific cultivation and the precision management by phenotyping detection. However, traditional measurement methods are time consuming and laborious, and the database is insufficient to meet the requirements of mass numbers. The High-throughput analysis on the potato crop phenotyping is urgent to solve such bottleneck. The morphometric and physiological parameters of plant are particularly interested in potato plant phenotyping detection, thus the research will conducted on the detection method of potato plant phenotyping based on feature extraction of pinnate leaf characteristics. In order to obtain the data, Multi-sensors will be used including time of flight 3D camera, hyper-spectral camera, fluorescence spectral imagery, photosynthetic rate meter, et al. The leaf area will be manually measured. The multi-dimensional data are analyzed. Firstly, the pinnate leaves will be extracted in three-dimensional spatial based on the fusion of 3D point cloud and color image. The area data of lobular and small crack leaves are measured by the spatial surface fitting. And then the leaf area index(LAI) value could be calculated which is key parameter to the morphological phenotypes. Secondly, the method of extracting weak signals of chlorophyll fluorescence radiation induced by sunlight will be studied. After established the detection model of chlorophyll fluorescence kinetic parameters based on hyper-spectrum, the relationship between hyper-spectral indices, chlorophyll content, photosynthetic rate and fluorescence parameters will be analyzed to indicate the photosynthetic difference between lobular and small crack leaves of pinnate leaf. Thirdly, multi-dimensional spectral and spatial imaging system will be build for the potato plant detection. The correlation phenotype groups of plant morphological and physiological characteristics will be extracted synchronously with LAI, chlorophyll content and fluorescence kinetic parameter. As a result, the spatiotemporal dynamic detection method and system could be established for the evaluation on the leaf growth stages. The conclusion will provide a rapid and nondestructive method with the accurate morphology measurement, deep understanding on physiological and biochemical phenotypes, and systematical phenotyping detection on potato plants.