Deep learning-based phenotyping of crop seed banks and herbaria

生物多样性是农业和生态系统长期可持续性的基础。目前的商业作物有一个极其狭窄的基因基础，这是一个弱点。种子库和植物标本库是经过数十年收集的集中资源，其中包括许多遗产品种，在面对气候变化和其他压力时具有潜在价值。挑战在于以一种对研究人员、育种者和农业有用的系统方式提取这些信息。我们假设这些物理档案材料可以使用非破坏性或最小破坏性的方法来挖掘有用性状的变异，包括基于图像的表型，这种变异与潜在的遗传学有关。为了验证这一点，在AberInnovation SeedBiobank和IBERS的作物标本室中可以找到记录良好的草和谷物收藏品。具体目标是：Obj1。机器学习工具为了开发强大的深度学习协议和测试，将使用一组从选定的测试草中手动标记的图像来训练神经网络来识别和测量特征。我们最近应用深度学习从凭证和uCT扫描中识别和计数器官（硕士项目，图）。这些深度学习网络将被扩展到在不同的图像分辨率水平上量化更广泛的特征。例如，更高分辨率成像和深度学习将应用于支持水运输和气体交换过程的气孔和血管等微观特征，并将评估其他最小破坏性模式（x射线荧光成像前期分析，营养成分代谢组学等）。obj2。为了正式解决这些性状的变异是否与潜在的遗传变异有关（因此对育种者有用），DR将使用从基因非结构化种群中获得的凭证，这些种群以前是通过选择燕麦和黑麦草对杂交产生的。这些形态多样化和遗传特征的群体为该方法的验证提供了严格的测试案例。学生将建立定量模型，以解释基因型对影响产量和质量的特征变异的贡献，并与使用传统数据产生的模型进行比较。草和燕麦草本植物的表型分析一个非常广泛的草物种收集由原始野生样本（具有地理坐标）以及在确定的农艺下当地种植的复制品组成。这些将被成像，并在适当的情况下进行uCT扫描，以确定种子和茎的性状。这些样本与育种分类账交叉参考，在许多情况下，它们的血统可以追溯到当前的商业品种，而种子可以根据需要恢复和再生。这10000个样本的物理收集提供了一个未开发的资源来检查遗传和环境的影响。该学生将专注于收集中的黑麦草和燕麦，旨在利用先进的遗传分析技术，如GWAS。理由和可能的结果：这个以作物为重点的项目汇集了阿伯里斯特威斯的表型学中心和种子库，以及萨里的计算机科学，他们之前曾与邱园合作研究生物多样性。这种专业知识与来自基因定义物种的大型数据集的结合，将允许设计出优化的方法，以提取和解释来自世界各地植物园的更广泛的集合的信息内容，并最终扩展到草以外的其他作物，并与生物多样性相关的更广泛的问题。

Biodiversity underpins long term sustainability of agriculture as well as ecosystems. Current commercialcrops have a extremely narrow genetic base, which is a vulnerability. Seed banks and Herbaria representa concentrated resource, collected over many decades and include many heritage varieties with potentialvalue in the face of climate change and other pressures. The challenge is to extract this information in asystematic manner that useful to researchers, breeders and agriculture.We hypothesize that these physical archival materials can be mined for variation in useful traits usingnon- or minimally destructive approaches, that include image-based phenotyping, and this variationrelated to the underlying genetics.To test this, well documented grass and cereal collections are available in the AberInnovation SeedBiobank and in the IBERS' Crop Herbarium. The specific objectives are to:Obj1. Machine learning toolsTo develop robust deep learning protocols and test, a set of manually labelled images from selected testgrasses will be used to train neural networks to recognise and measure features. We have recentlyapplied deep learning to identify and count organ from both vouchers and uCT scanning (Masters project,Fig). These deep learning networks will be extended to quantify a wider variety of traits at differentlevels of image resolution. For example, higher resolution imaging and deep learning will be applied tomicroscopic features such as stomata and vessels that support water transport and gaseous exchangeprocesses and other minimally destructive modalities will be evaluated (X-Ray Fluorescence imaging forelemental analyses, metabolomics for nutritive content, etc).Obj2. De novo test-case experimentsTo formally address whether variation in these traits can be linked to underlying genetic variation (andtherefore be useful to breeders), the DR will use vouchers made from genetically unstructuredpopulations that have been previously created by crossing selected pairs of oats and of ryegrass. Thesemorphologically diverse and genetically characterised populations provide a rigorous test-case for validation of the approach. The student will build quantitative models to account for the genotypiccontribution to variation in features contributing to yield and quality, and compare to models producedusing conventional data.Obj3. Phenotyping the grass and oat herbariaA very extensive collection of grass species is populated by original wild samples (with geographiccoordinates) as well as duplicates grown locally under defined agronomy. These will be imaged and,where appropriate, uCT scanned for seed and stem traits. The samples are crossed referenced tobreeding ledgers and in many cases their pedigree can be traced through to current commercial varietieswhile seed can be recovered and regrown as necessary. This physical collection of >10000 samplesprovides an unexploited resource to examine genetic and environmental effects across time. The studentwill focus on the ryegrass and oats within the collection aiming to exploit advanced genetic analyticaltechniques such as GWAS.Justification and Likely Outcomes: This crop-focused project brings together the Phenomics Centre andSeed Bank at Aberystwyth with Computer Sciences in Surrey who have previously worked with KewGardens on biodiversity. This combination of expertise combined with large datasets from geneticallydefined species will allow design of optimised approaches to extract and interpret the informationcontent of more extensive collections, available in Botanical Gardens from across the world, andultimately extending beyond the grasses to other crops and into wider questions associated withbiodiversity.