(AFS) Developing a deep learning-based 3D imaging platform for tracking and modelling whole plant growth responses to environmental &chemical stresses

(AFS) 开发基于深度学习的 3D 成像平台，用于跟踪和建模整个植物生长对环境的响应

• 批准号: 2111835
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-2111835
• 关键词: AFS; Developing; deep; learning; based

There is an urgent need for novel technologies that allow scientists and agronomists to monitor and predict the combined impacts of environmental stresses (abiotic and biotic) on plant growth and performance. The capacity to track relevant growth traits atthe whole-plant level is significantly increased if 3D data is available, which can be used to understand and model the response of a given genotype to a given environment. Recently, photometric stereo (PS) 3D imaging has been shown to be able to resolve changes in leaf surface textures and morphology induced by different stresses that are not detectable using 2D imaging methods (ongoing BBSRC funded work in SBS and CMV). PS offers unprecedented spatial resolution for 3D data and is inherently low-cost. Working in amultidisciplinary environment, the candidate will develop deep-learning approaches to automate the extraction of trait characteristics from PS data and build a unique, field-level image analysis platform to accurately determine the impact of different chemical treatments on different genotypes based on their growth environment.Project plan.Methods will be validated initially in the model species Arabidopsis thaliana, after which the candidate will investigate a range of larger grass and broadleaf plants. A specific focus will be on wheat cultivars under glasshouse and field conditions. The Williams' group (GeoSciences) will provide support for conducting wheat field analyses from the BBSRC-NERC funded SARIC project BB/P004628/1. The availability of chemically treated and untreated wheat field trials will facilitate field testing of PS systems, and PS data will be complemented by available drone-based image datasets (RGB, thermal and multispectral) where "structure from motion" approaches have been used to reveal 3D crop canopy traits.The candidate will investigate if combining trait data from these different datasets will help to improve robustness in detecting key response functions, which then could be added to available crop models to better predict the sensitivity of wheat to combined stresses and optimise the timing of chemical treatments.Intended impact.The PS system will be developed as a high-throughput tool to analyse the impact of different chemical treatments on Arabidopsis grown in differing temperature, light or droughted environments. Work in Arabidopsis will also allow investigation of treatments on vulnerable genotypes known to affect growth. Subsequent work in wheat trails will test the real-world capacity of the PS system to provide farmers with useful data, such as early stage detection of stress and/or disease and vulnerability of particular genotypes, and improving the efficiency and sustainability of chemical treatment applications.

迫切需要新的技术，使科学家和农学家能够监测和预测环境胁迫（非生物和生物）对植物生长和性能的综合影响。如果3D数据可用，则在整个植物水平上跟踪相关生长性状的能力显着增加，该数据可用于理解和模拟给定基因型对给定环境的响应。最近，光度立体（PS）3D成像已被证明能够解决不同的压力，不能检测使用2D成像方法（正在进行的BBSRC资助的SBS和CMV的工作）引起的叶表面纹理和形态的变化。PS为3D数据提供了前所未有的空间分辨率，并且成本低。在多学科环境中工作，候选人将开发深度学习方法，以自动从PS数据中提取性状特征，并建立一个独特的田间图像分析平台，以准确确定不同化学处理对不同基因型的影响，基于其生长环境。项目计划。方法将在模式物种拟南芥中进行初步验证，之后，候选人将调查一系列较大的草和阔叶植物。一个具体的重点将是在温室和田间条件下的小麦品种。威廉姆斯小组（地球科学）将为BBSRC-NERC资助的SARIC项目BB/P004628/1的麦田分析提供支持。化学处理和未处理麦田试验的可用性将促进PS系统的田间测试，PS数据将得到可用的基于无人机的图像数据集的补充（RGB，热和多光谱），其中“从运动的结构”候选人将研究结合这些不同数据集的性状数据是否有助于提高检测关键响应的鲁棒性功能，然后可以添加到现有的作物模型，以更好地预测小麦对组合胁迫的敏感性，并优化化学处理的时机。预期的影响。PS系统将被开发为一个高通量工具，用于分析不同温度，光照或干旱环境下生长的拟南芥不同化学处理的影响。在拟南芥中的工作也将允许对已知影响生长的脆弱基因型的治疗进行调查。小麦试验的后续工作将测试PS系统为农民提供有用数据的真实能力，例如早期检测特定基因型的压力和/或疾病和脆弱性，以及提高化学处理应用的效率和可持续性。