(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.

迫切需要新型技术，使科学家和农艺师可以监测和预测环境应力（非生物和生物生物）对植物生长和性能的综合影响。如果有3D数据可用，可以将整个工厂水平上相关增长性状的能力显着提高，该数据可用于理解和建模给定基因型对给定环境的响应。最近，已经证明光度法立体声（PS）3D成像能够解决叶片表面纹理和形态的变化，这些变化是由使用2D成像方法（正在进行的BBSRC资助的SBS和CMV的工作）无法检测到的不同应力引起的。 PS为3D数据提供了前所未有的空间分辨率，并且本质上是低成本的。候选人将在amultidecoligary的环境中工作，将开发深入学习的方法来自动从PS数据中提取特征特征，并建立一个独特的，现场的图像分析平台，以准确确定不同化学处理对不同基因型的影响基于其增长环境的不同基因型的影响。在其范围内，将在较大的水平范围内验证，并将在较大的水平上进行验证。特定的重点将放在玻璃厂和田间条件下的小麦品种上。威廉姆斯集团（Geosciences）将为BBSRC-NERC资助的Saric Project BB/P004628/1进行小麦现场分析提供支持。接受化学处理和未经处理的小麦现场试验的可用性将促进PS系统的现场测试，PS数据将通过可用的基于无人机的图像数据集（RGB，热和多光谱）进行补充，其中使用“来自运动的结构”的方法，其中已使用“从运动结构”来揭示3D作物cansopy的良好范围，以调查这些不同的数据范围。对于可用的作物模型，以更好地预测小麦对结合压力并优化化学处理时间的敏感性。意义上的影响。PS系统将作为一种高通量工具开发，以分析不同化学处理对在不同温度，光或光线环境中生长的拟南芥的影响。拟南芥的工作还将允许研究已知会影响生长的脆弱基因型的治疗方法。随后在小麦径中进行的工作将测试PS系统的现实能力，可为农民提供有用的数据，例如对压力和/或疾病的早期发现以及特定基因型的脆弱性，并提高化学处理应用的效率和可持续性。