Machine learning based image analysis for phenotyping to speed up barley breeding

o基于图像的自动植物表型分析允许通过分析传感器和相机在受控环境中或在任何定义的时间间隔在田间捕获的图像来高通量量化植物性状。可从不同视角获取可见光、荧光、近红外、红外和高光谱图像，从而构建植物及其组成部分（如叶、茎、花和穗等）的2D和3D模型。以及叶的胁迫水平，从而能够以高通量方式进行非破坏性植物表型分析。结合生物和非生物胁迫实验，这些在多个时间点和条件下拍摄的图像将显示植物生长速率、茎伸长速度和叶角轨迹等性状的动态变化，从而为寻找适应气候变化的解决方案提供关键数据拟议的研究将允许更准确的表型分析，提高分辨率，并大大降低劳动力成本。这项技术将大大加速和加强培育具有有益结构和生理性状的改良作物。

o Automated image-based plant phenotyping allows high throughput quantification of plant traits by analysing images captured by sensors and cameras in controlled environments or in the fields at any defined time interval. Visible light, fluorescent, near infrared, infrared and hyperspectral image can be captured from different viewing angles allowing the construction of 2D and 3D model of plants and their components, e.g. leaves, stems, flowers and spikes etc. Morphological and structure information about the plant, such as plant height and volumetric biomass, as well as physiological changes, such as temperature, water content, as well as the stress levels of leaves thus could be inferred, allowing non-destructive plant phenotyping in a high-throughput manner. Combined with experiments studying biotic and abiotic stresses, these images taken at multiple time points and conditions will illustrate the dynamic changes of the traits providing valuable information, such as the plant growth rate, stem elongation speed and trajectories of leave angle etc, thus providing key data for finding solutions for adaptation to climate change (e.g. drought, waterlogging, pathogen) and reduce the input for agriculture (e.g. fertilizers and pesticides).The proposed study will allow more accurate phenotyping with increased resolution and significantly reduced labour costs. This technology will greatly accelerate and enhance breeding of improved crops with beneficial architectural and physiological traits.