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

o通过分析传感器和相机在受控环境中或在任何规定的时间间隔内捕获的图像，自动化的基于图像的植物表型分析允许对植物性状进行高通量定量分析。可见光、荧光、近红外、红外和高光谱图像可以从不同的视角捕获，从而构建植物及其成分的二维和三维模型，例如叶子、茎、花和穗等。因此，可以推断植物的形态和结构信息，如植物高度和体积生物量，以及生理变化，如温度、含水量以及叶片的胁迫水平，从而以高通量的方式实现非破坏性植物表型。结合生物胁迫和非生物胁迫的实验研究，这些在多个时间点和条件下拍摄的图像将揭示植物性状的动态变化，为植物生长速度、茎伸长速度和叶片角轨迹等提供有价值的信息，从而为寻找适应气候变化（如干旱、内涝、病原体）的解决方案和减少农业投入（如化肥和农药）提供关键数据。拟议的研究将允许更准确的表型，提高分辨率和显着降低劳动力成本。这项技术将大大加快和加强具有有益结构和生理性状的改良作物的育种。

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.