MRI: Development of a PhenoNet - an Integrated Robotic Network for Field-based Studies of Genotype x Environment Interactions

授予爱荷华州州立大学开发和部署PhenoNet -一个集成的机器人网络，用于基因型与环境（GxE）相互作用的实地研究。PhenoNet的核心组件是一套PhenoBots;轻量级机器人能够使用GPS和本地范围传感器在作物行之间自主导航，同时采用先进的传感技术来表现作物植物。PhenoBots可以测量指标，如茎大小，植物高度，叶片角度和雄穗/花序特性随着时间的推移。这些机器人将针对玉米研究进行优化，并且可以很容易地适应其他行栽作物。该网络（PhenoNet）是一个通用平台，可以对基因型和环境相互作用进行全面的实地研究。该项目的广泛影响有三个方面。首先，PhenoNet将对社会产生重要影响，因为了解X基因组与环境的相互作用将有助于解决地球上不断增长的人口对充足食物、饲料和纤维的需求，这在不断变化的环境中至关重要。PhenoNet将通过巩固植物科学家和工程师之间的联系，将“大数据”更深入地引入农业，以实现这一目标。第二，这个项目与NSF-NRT项目“植物的预测表型组学”是协同的，该项目最近被授予爱荷华州州立大学。在这个主要研究仪器项目概述的研究和工程将提供一个优秀的机会，从工程学科，计算机科学，统计学和农学的学生合作，并从事国家的最先进的跨学科研究。该项目还将促进对现有工程师和植物科学家的培训，他们在网络，机器人和农学方面经验丰富。第三，该项目将面向代表性不足的群体，以少数民族服务机构为目标招收学生，并将与女工程师协会和其他类似团体合作，寻求妇女参与者，以帮助实现NSF-NRT奖扩大参与的努力。PhenoBots是农业和技术领域的一个重要和基本的进步，因为它们可以更有效地表征高大植物的成熟过程。以前的技术和平台要么是没有能力，要么是受到各种限制的极大阻碍。Phenobots的设计改进使机器人更加坚固，稳定，轻便，集成和经济。这为变革性研究创造了一条途径，因为它能够随着时间的推移对玉米等高大作物的性状进行原位非侵入性监测。PhenoNet将由四个PhenoBots组成的网络组成，这些PhenoBots将由爱荷华州、堪萨斯、明尼苏达州、内布拉斯加州和威斯康星州的植物科学家部署。从高通量表型分析产生的数据将解决是否有可能在特定环境中预测给定基因型的表型。

An award is made to Iowa State University to develop and deploy PhenoNet - an integrated robotic network for field-based studies of genotype crossed with environment (GxE) interactions. The core component of PhenoNet is a set of PhenoBots; lightweight robots that are able to autonomously navigate between crop rows using GPS and local range sensors while employing advanced sensing technologies to phenotype crop plants. The PhenoBots can measure indicators such as stalk size, plant height, leaf angle and tassel/inflorescence properties over time. The robots will be optimized for maize research and can be easily adapted for other row crops. The network (PhenoNet) is a universal platform which enables comprehensive field-based research on genotype and environment interactions. The broader impacts of this project are threefold. First, PhenoNet will have an important impact on society as understanding genome X environment interactions will help address the need for sufficient food, feed, and fiber for the planet's growing population, which is vital in an ever-changing environment. PhenoNet will bring "big data" more deeply into agriculture by cementing connections between plant scientists and engineers in their efforts to reach this goal. Second, this project is synergistic with the NSF-NRT project, "Predictive Phenomics of Plants", recently awarded to Iowa State University. The research and engineering outlined in this Major Research Instrumentation project will provide an outstanding opportunity for students from engineering disciplines, computer science, statistics, and agronomy to collaborate and engage in state-of-the-art interdisciplinary research. This project will also advance the training of current engineers and plant scientists who are experienced with networking, robotics and agronomy. Third, this project will reach out to underrepresented groups by targeting minority-serving institutions for student recruitment and will work with the Society of Women Engineers and other similar groups in seeking women participants to help meet the NSF-NRT award's efforts to broaden participation. The PhenoBots are an important and essential advancement in the fields of agriculture and technology because they more efficiently characterize tall plants over time to their maturity. Previous technology and platforms are either incapable of, or are greatly hindered by various constraints. The design improvements of the Phenobots enable the robots to be more robust, stable, lightweight, integrated and economical. This creates a pathway for transformative research as it enables in situ, non-invasive monitoring of the traits of tall crops, like maize, over time. PhenoNet will consist of a network of four PhenoBots, which will be deployed by plant scientists in Iowa, Kansas, Minnesota, Nebraska, and Wisconsin. The data generated from high throughput phenotyping will address whether it is possible to predict the phenotype of a given genotype in a specified environment.