A Field-based High Throughput Phenotyping Platform for Plant Genetics

PI：杰西·波兰（堪萨斯州立大学/USDA-ARS）联合PI：凯文·普莱斯（堪萨斯州立大学）合作者：迈克尔·戈尔，安德鲁·弗兰奇，凯利·索普，杰夫·怀特（USDA-ARS）；Pedro Andrade-Sanchez（亚利桑那大学）；Allan Fritz, Randy Price, William Schapaugh, Stephen Welch, Naiqian Zhang (Kansas State University)：植物生物学的本质是了解遗传变异和观察到的表型之间的联系。虽然基因分型现在可以用低成本的下一代测序技术来完成，但表型分型是劳动密集型的，并且已经成为植物生物学研究和作物改良计划的限制因素。该项目将通过创建快速评估多种数量植物性状的平台，开发基于田间的高通量表型（HTP）的转化能力。近端传感工具和GPS参考将集成在一起，以开发低成本、灵活、坚固且适用于各种物种的移动平台。这项工作的一个中心前提是，整合来自多种类型传感器的数据流将提高田间种植植物可量化特征的准确性和数量。因此，第二个目标是开发新的算法来分析HTP数据，以定量估计田间环境中表达的表型。新开发的传感器系统、指数和算法将用于检测小麦、大豆和棉花的多种数量性状的测试案例。HTP平台的发展将补充目前正在收集的基因组信息的爆炸式增长，并将在以前不可能的规模上实现现场水平的实验生物学。由于该项目的重点是对代表不同植物类型和生产系统的经济上重要的作物物种进行实地评估，因此该项目开发的平台和分析方法有望广泛应用于一系列目标环境和物种，并允许可扩展性进行更大规模的研究。开发的工具和方法将通过实践培训讲习班转让给科学界。培训材料、设备设计和软件将通过项目网站公开提供。该项目将为研究生和研究生提供培训，同时为HTP领域的工程和植物科学本科生提供多个实习机会。

PI: Jesse Poland (Kansas State University/USDA-ARS)Co-PIs: Kevin Price (Kansas State University)Collaborators: Michael Gore, Andrew French, Kelly Thorp, Jeff White (USDA-ARS); Pedro Andrade-Sanchez (University of Arizona); Allan Fritz, Randy Price, William Schapaugh, Stephen Welch, Naiqian Zhang (Kansas State University)The essence of plant biology is to understand the link between genetic variation and observed phenotypes. While genotyping can now be accomplished with low-cost, next-generation sequencing technologies, phenotyping is labor intensive and has become the limiting factor in plant biology studies and crop improvement programs. This project will develop transformational capacity for field-based high throughput phenotyping (HTP) by creating platforms for rapid assessment of multiple quantitative plant traits. Proximal sensing tools and GPS referencing will be integrated to develop mobile platforms that are low-cost, flexible, robust, and amenable for a range of species. A central premise of this work is that integrating streams of data from multiple types of sensors will increase both the accuracy and number of traits that can be quantified for field grown plants. Thus, a second objective is to develop novel algorithms for analyzing HTP data to quantitatively estimate phenotypes expressed in field environments. The newly developed sensor systems, indexes, and algorithms will be applied in test cases that examine multiple quantitative traits in wheat, soybean and cotton. The development of HTP platforms will complement the explosion of genomic information currently being collected and will enable field level experimental biology on a scale that has not been previously possible. Because the focus of this project is on field-based assessment of economically important crop species that represent different plant types and production systems, the platform and analytic methods developed in this project are expected to have broad application to a range of target environments and species and allow scalability for larger studies. The tools and methods developed will be transferred to the scientific community through hands-on training workshops. Training materials, equipment designs and software will be publicly available through a project website. This project will provide training for graduate and postgraduate scholars while hosting multiple internships for undergraduate engineering and plant science students in the field of HTP.