LeMuR: Plant Root Phenotyping via Learned Multi-resolution Image Segmentation

LeMuR：通过学习的多分辨率图像分割进行植物根表型分析

• 批准号: BB/P026834/1
• 负责人: Michael Pound
• 金额: $ 18.25万
• 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: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FP026834%2F1
• 关键词: LeMuR; Plant; Root; Phenotyping; via

Plant phenotyping - the measurement of quantitative data on plant structure and function from image and sensor data - is a key bottleneck holding back efforts towards global food security; that is, providing enough food for a growing population. The roots of food crops are clearly important for the development of the crop itself, yet root phenotyping is particularly challenging, as the roots grow in soil. Though methods of imaging roots in soil are emerging, they remain slow and expensive. Large-scale experiments are still performed using artificial growth media (gel, filter paper etc.) that allow the root to be imaged using conventional equipment. Analysis of the resulting images requires the root to be separated from its background and a structural description of the root architecture to be produced and presented to the user. But doing this fully automatically is a challenge, and most software tools written to date work with very specific sets of images, and tend to break if used outside of the scenarios they were designed for.In this proposal we will develop cutting-edge deep learning analysis approaches to build a much more general software tool. So-called deep approaches are revolutionising image analysis, with large companies developing similar techniques to analyse other image sets, such as for diagnosing medical conditions, to great effect. The proposed approach, LeMuR (Learned Multi-Resolution image segmentation), will exploit the common structure of root image analysis tasks, and recent advances in deep machine learning, to produce a flexible plant root phenotyping tool that can be easily adapted, without re-writing code, to new laboratory environments and imaging techniques.We propose two main developments. First, a software tool LeMuRoot which will be designed to work across a wide variety of root system data sets right out of the box, compared to the limited application of traditional tools. Second, a software framework (LeMuRLearn) to allow biologists themselves to adapt the tool to even more images beyond those that LeMuRoot was designed to work with. By supplying their own image data sets annotated using a novel user interface which will form part of LeMuRLearn, biologists will be able to re-train the core model underlying the tools, allowing them to improve the quality of results for their particular data. In a further novel process, biologists will be able to seamlessly share their newly trained tool with the community, which in turn can be used as a base for further development. This will allow LeMuRLearn to incrementally improve over time, and for it to use different underlying models for a wider variety of image data sets than was conceived of at initial release.This is exciting for two main reasons. First, previous development of software tools has by necessity been limited to computer scientists who are capable programmers - here we put the continued development of the tool in the hands of the biology community themselves. Second, by sharing the core model underlying the tool (called the LeMuRNet), biologists can share with the community without fear of sharing raw data or results.Combined with hiding the computational complexity of both the analysis and evolution process behind an accessible user interface, the potential to disrupt the current process for image analysis tool development and use with plant science (and beyond) is high.

植物表型 - 对图像和传感器数据的植物结构和功能的定量数据的测量 - 是阻碍全球粮食安全的努力的关键瓶颈；也就是说，为不断增长的人口提供足够的食物。食品作物的根源对于作物本身的发展显然很重要，但是随着土壤中的根生长，根表型尤其具有挑战性。尽管出现了在土壤中成像根的方法，但它们仍然缓慢且昂贵。大规模实验仍使用人工生长培养基（凝胶，滤纸等）进行，这些培养基允许使用常规设备对根进行成像。对所得图像的分析需要将根与其背景分开，并将其对根体系结构的结构描述产生并呈现给用户。但是，完全自动做到这一点是一个挑战，并且大多数软件工具都编写为迄今为止非常具体的图像，并且如果在他们设计的方案之外使用，往往会破裂。在此提案中，我们将开发最先进的深度学习分析方法来构建更通用的软件工具。所谓的深层方法正在彻底改变图像分析，其中大公司开发了类似的技术来分析其他图像集，例如诊断医疗状况，从而产生了很大的影响。所提出的方法，Lemur（学习的多分辨率图像分割），将利用根部图像分析任务的共同结构以及深度机器学习的最新进展，以生成一种柔性的植物根表型工具，该工具可以轻松地适应，而无需重新编写代码，以适应新的实验室环境和成像技术。首先，与传统工具的有限应用相比，该软件工具Lemuroot将旨在在开箱即用的各种根系数据集上使用。其次，一个软件框架（Lemurlearn）允许生物学家自己调整该工具，以使更多的图像超出Lemuroot设计的图像。通过提供自己的图像数据集使用新颖的用户界面注释，该数据集将构成Lemurlearn的一部分，生物学家将能够重新培训工具基础的核心模型，从而使他们能够提高其特定数据的结果质量。在进一步的新颖过程中，生物学家将能够与社区无缝共享他们新训练的工具，而社区可以用作进一步发展的基础。这将使Lemurlearn能够随着时间的推移逐步改善，并且要使用不同的基础模型来用于更广泛的图像数据集，而不是初始版本所想象的。这是令人兴奋的，原因有两个。首先，以前的软件工具的开发仅限于具有能力的程序员的计算机科学家 - 在这里，我们将工具的持续开发置于生物学社区本身的手中。其次，通过共享该工具（称为Lemurnet）的核心模型，生物学家可以与社区共享，而不必担心共享原始数据或结果。隐藏了分析和可访问用户界面背后的分析和进化过程的计算复杂性，这可能会破坏当前的图像分析工具开发和与工厂科学的使用（以及超越工厂科学（以及更大））。

项目成果

Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat

• DOI: 10.1016/s2095-3119(21)63700-0
• 发表时间: 2022-03-15
• 期刊: JOURNAL OF INTEGRATIVE AGRICULTURE
• 影响因子: 4.8
• 作者: Griffiths, Marcus;Atkinson, Jonathan A.;Wells, Darren M.
• 通讯作者: Wells, Darren M.

Predicting Plant Growth from Time-Series Data Using Deep Learning

• DOI: 10.3390/rs13030331
• 发表时间: 2021-02-01
• 期刊: REMOTE SENSING
• 影响因子: 5
• 作者: Yasrab, Robail;Zhang, Jincheng;Pound, Michael P.
• 通讯作者: Pound, Michael P.

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

• DOI: 10.1101/709147
• 发表时间: 2019-07
• 期刊: GigaScience
• 影响因子: 9.2
• 作者: R. Yasrab;J. Atkinson;D. Wells;A. French;T. Pridmore;Michael P. Pound

X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil

• DOI: 10.1002/ppj2.20036
• 发表时间: 2022-01-01
• 期刊: The Plant Phenome Journal
• 作者: Griffiths, M.

Uncovering the hidden half of plants using new advances in root phenotyping.

• DOI: 10.1016/j.copbio.2018.06.002
• 发表时间: 2019-03
• 期刊: Current opinion in biotechnology
• 影响因子: 7.7
• 作者: Atkinson JA;Pound MP;Bennett MJ;Wells DM
• 通讯作者: Wells DM