PhenomUK - Crop Phenotyping: from Sensors to Knowledge

PhenomUK - 作物表型分析：从传感器到知识

• 批准号: MR/R025746/1
• 负责人: Tony Pridmore
• 金额: $ 67.35万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FR025746%2F1
• 关键词: PhenomUK; Crop; Phenotyping; Sensors; Knowledge

Major improvements in crop performance are needed to keep pace with population growth, which is driving up global food demand, and climate change, which is increasing the vulnerability of cropping systems to extreme weather events in the UK and internationally. Whilst plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e. the structure and function of plants) associated with their genetic structure and environment remains a major bottleneck. Solutions will be multidisciplinary, requiring engineering and computer science as well as plant biology. Sensors are required that can measure of a diverse range of properties of complex, deformable, living and growing objects - plants - often in quite hostile environments. Structural and functional plant traits capturing key properties of roots, shoots, flowers, seeds, etc. must be recovered from those measures effectively and efficiently. Most data capture is image-based, requiring advanced computer vision techniques. Trait data are then used to provide understanding of e.g. plants' growth rates, flowering times, seed production and yield, using advanced data analysis and machine learning methods.PhenomUK's vision is of an integrated, mutually-informed community of computer scientists, engineers and biologists, working together across discipline boundaries to shape the technologies needed to address one of the global challenges of the 21st century - the provision of a secure food supply to a growing population against a background of resource depletion and climate change. Through series of Annual Conferences, workshops, networking visits, online training events and pump-priming research projects, this multidisciplinary network project will:1. ensure that UK scientists have access to the innovative technological capabilities needed to drive world-leading basic discovery research in the plant, crop and agricultural sciences2. provide the deeper understanding of national plant phenotyping capabilities, needs and opportunities required to allow the UK to participate fully in and gain maximum benefit from international initiatives such as the pan-European infrastructure being created by the ESFRI EMPHASIS project (https://emphasis.plant-phenotyping.eu).

作物性能需要大幅改善，以跟上人口增长的步伐，这推动了全球粮食需求的增长，以及气候变化，这增加了种植系统对英国和国际极端天气事件的脆弱性。虽然植物育种工作极大地受益于基因组学的进步，但与其遗传结构和环境相关的作物表型（即植物的结构和功能）分析仍然是一个主要瓶颈。解决方案将是多学科的，需要工程和计算机科学以及植物生物学。传感器需要能够测量复杂的、可变形的、有生命的和生长的物体（植物）的各种特性，这些物体通常处于非常恶劣的环境中。必须有效和高效地从这些措施中恢复具有根、芽、花、种子等关键特性的植物结构和功能性状。大多数数据采集是基于图像的，需要先进的计算机视觉技术。然后，利用先进的数据分析和机器学习方法，利用性状数据来了解植物的生长速率、开花时间、种子产量和产量等。PhenomUK的愿景是建立一个由计算机科学家、工程师和生物学家组成的综合、相互了解的社区。跨越学科界限共同努力，塑造解决21世纪世纪全球挑战之一所需的技术-在资源枯竭和气候变化的背景下，为不断增长的人口提供安全的粮食供应。通过一系列的年度会议，研讨会，网络访问，在线培训活动和泵启动研究项目，这个多学科网络项目将：1。确保英国科学家能够获得推动植物、作物和农业科学领域世界领先的基础发现研究所需的创新技术能力2。提供更深入的了解国家植物表型能力，需求和机会，使英国能够充分参与并从国际倡议中获得最大利益，例如ESFRI EMPHASIS项目（https：//www.example.com）创建的泛欧基础设施。emphasis.plant-phenotyping.eu

项目成果

Data management challenges for artificial intelligence in plant and agricultural research.

• DOI: 10.12688/f1000research.52204.2
• 发表时间: 2021
• 期刊: F1000Research

How can computers help grow crops better

计算机如何帮助农作物更好地生长

• 发表时间: 2021
• 作者: Futurum

Meeting sustainable development goals via robotics and autonomous systems.

• DOI: 10.1038/s41467-022-31150-5
• 发表时间: 2022-06-21
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Guenat, Solene;Purnell, Phil;Davies, Zoe G.;Nawrath, Maximilian;Stringer, Lindsay C.;Babu, Giridhara Rathnaiah;Balasubramanian, Muniyandi;Ballantyne, Erica E. F.;Bylappa, Bhuvana Kolar;Chen, Bei;De Jager, Peta;Del Prete, Andrea;Di Nuovo, Alessandro;Ehi-Eromosele, Cyril O.;Eskandari Torbaghan, Mehran;Evans, Karl L.;Fraundorfer, Markus;Haouas, Wissem;Izunobi, Josephat U.;Jauregui-Correa, Juan Carlos;Kaddouh, Bilal Y.;Lewycka, Sonia;MacIntosh, Ana C.;Mady, Christine;Maple, Carsten;Mhiret, Worku N.;Mohammed-Amin, Rozhen Kamal;Olawole, Olukunle Charles;Oluseyi, Temilola;Orfila, Caroline;Ossola, Alessandro;Pfeifer, Marion;Pridmore, Tony;Rijal, Moti L.;Rega-Brodsky, Christine C.;Robertson, Ian D.;Rogers, Christopher D. F.;Rouge, Charles;Rumaney, Maryam B.;Seeletso, Mmabaledi K.;Shaqura, Mohammed Z.;Suresh, L. M.;Sweeting, Martin N.;Taylor Buck, Nick;Ukwuru, M. U.;Verbeek, Thomas;Voss, Hinrich;Wadud, Zia;Wang, Xinjun;Winn, Neil;Dallimer, Martin
• 通讯作者: Dallimer, Martin

TTL Networks e-Event 2021 Report

TTL Networks 2021 年电子活动报告

• 发表时间: 2021
• 作者: Hayes, C