室内植物表型平台及性状鉴定研究进展和展望

Indoor phenotyping platforms and associated trait measurement:Progress and prospects

植物表型组学研究正逐渐向综合化、规模化、多尺度和高通量的方向快速发展。本文首先介绍了植物表型研究的最新动向。然后针对室内表型监测平台的特点和各类室内表型针对的表型性状进行了系统介绍,包括产量、品质、胁迫抗性(包括干旱、抗冷热、盐胁迫、重金属和病虫害)等。在此基础上,本文还根据通量、传感器集成度和平台大小等把一些国内外流行的室内植物表型平台进行了分类,并介绍了这些室内表型平台在植物研究中的应用情况。同时,本文还介绍了室内表型数据的管理和解析方法。最后,本文着重讨论了室内表型平台的发展方向,并结合中国植物研究的实际情况对表型组学在中国的发展提出了展望,以期为中国植物表型研究提供指导和建议。

Plant phenomics is under rapid development in recent years,a research field that is progressing towards integration,scalability,multi-perspectivity and high-throughput analysis.Through combining remote sensing,Internet of Things(IoT),robotics,computer vision,and artificial intelligence techniques such as machine learning and deep learning,relevant research methodologies,biological applications and theoretical foundation of this research domain have been advancing speedily in recent years.This article first introduces the current trends of plant phenomics and its related progress in China and worldwide.Then,it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments,including yield,quality,and stress related traits such as drought,cold and heat resistance,salt stress,heavy metals,and pests.By connecting key phenotypic traits with important biological questions in yield production,crop quality and Stress-related tolerance,we associated indoor phenotyping hardware with relevant biological applications and their plant model systems,for which a range of indoor phenotyping devices and platforms are listed and categorized according to their throughput,sensor integration,platform size,and applications.Additionally,this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis.For example,ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments,PHIS and CropSight for managing complicated datasets,and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV,Scikit-Image,MATLAB Image Processing Toolbox.Finally,due to the importance of extracting meaningful information from big phenotyping datasets,this article pays extra attention to the future development of plant phenomics in China,with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes,the cultivation of cross-disciplinary researchers to lead the next-generation plant research,as well as the collaboration between academia and industry to enable world-leading research activities in the near future.