植物表型组学:发展、现状与挑战

随着遥感、机器人技术、计算机视觉和人工智能的发展,植物表型组学研究已经步入了快速成长阶段。本文首先介绍了植物表型组学的发展简史,包括其理论核心、研究方法、在生物研究中的应用以及国际上最新的研究动向。然后,针对各类表型技术载体平台如手持、人载、车载、田间实时监控、大型室内外自动化平台和航空机载等,分析这些技术手段在室内、外植物研究中的应用情况和实际问题。为了对表型研究中产生的巨量图像和传感器数据进行量化分析,把大数据转化为有实际意义的性状信息和生物学知识,本文着重讨论了后期表型数据解析和相应的研发过程。最后,提出表型组学的应用前景与未来展望,以期为中国的表型研究提供指导和建议。

Identification of Differentially Senescing Mutants of Wheat and Impacts on Yield,Biomass and Nitrogen Partitioning

Increasing photosynthetic capacity by extending canopy longevity during grain filling using slow senescing stay-green genotypes is a possible means to improve yield in wheat. Ethyl methanesulfonate （EMS） mutated wheat lines （Triticum aestivum L. cv. Paragon） were screened for fast and slow canopy senescence to investigate the impact on yield and nitrogen partitioning. Stay-green and fast-senescing lines with similar anthesis dates were characterised in detail. Delayed senescence was only apparent at higher nitrogen supply with low nitrogen supply enhancing the rate of senescence in all lines. In the stay-green line 3 （SG3）, on a whole plant basis, tiller and seed number increased whilst thousand grain weight （TGW） decreased; although a greater N uptake was observed in the main tiller, yield was not affected. In fast-senescing line 2 （FS2）, yield decreased, principally as a result of decreased TGW. Analysis of N-partitioning in the main stem indicated that although the slow-senescing line had lower biomass and consequently less nitrogen in all plant parts, the proportion of biomass and nitrogen in the flag leaf was greater at anthesis compared to the other lines; this contributed to the grain N and yield of the slow-senescing line at maturity in both the main tiller and in the whole plant. A field trial confirmed senescence patterns of the two lines, and the negative impact on yield for FS2 and a positive impact for SG3 at low N only. The lack of increased yield in the slow-senescing line was likely due to decreased biomass and additionally a possible sink limitation.

An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat

Wheat is one of the major crops in the world,with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply.The continual pressure to sustain wheat yield due to the world’s growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments.We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour.We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties.Based on these image series,we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds.As a first step towards robust measurement of key yield traits in the field,we present a promising approach that employ Fully Convolutional Network(FCN)to performsemantic segmentation of images to segment wheat spike regions.We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets.We found that the FCN architecture had achieved a Mean classification Accuracy(MA)>82%on validation data and>76%on test data and Mean Intersection over Union value(MIoU)>73%on validation data and and>64%on test datasets.Through this phenomics research,we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike,which can be used to assist yield-focused wheat breeding objectives in near future.