GCR: Accelerating Progress Toward Intrinsic Genetic Solutions to Sustainable Agricultural Intensification

GCR：加速实现可持续农业集约化的内在遗传解决方案

• 批准号: 1934481
• 负责人: Andrew Paterson
• 金额: $ 359.12万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934481&HistoricalAwards=false
• 关键词: GCR; Accelerating; Progress; Toward; Intrinsic

This Growing Convergence Research project aims at crop improvements through deep integration across life sciences (plant biology, agronomy, genetics), physical sciences (engineering), and computational sciences. Specific goals are to develop tools and apply methods that capitalize on advances in these respective fields to provide acceleration of crop improvement progress -- an outcome of worldwide importance.The proposers seek to build on their early progress in measurement of plant architecture, increasing scale to accommodate large populations, accelerating data processing and enhancing scientific visualization to efficiently pair imagery with rich information provided by post-processing algorithms. Further, it is important to go beyond quantification of growth trajectories and link these to measurements of crop yield, to reveal subsets of measurements that are accurate proxies for yield, and DNA markers that are diagnostic of salient genes. This multidisciplinary project is closely tied to the education of students and the public at many levels. The researchers will build on their long and positive involvement with talented members of underrepresented groups.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个不断增长的研究项目旨在通过跨生命科学（植物生物学，农艺学，遗传学），物理科学（工程）和计算科学的深层整合来改善作物。 具体目标是开发工具和应用这些方法，这些方法利用这些各自领域的进步，以加速作物改进的进步 - 全球重要性的结果。提案者试图建立在植物架构测量植物建筑方面的早期进步，增加规模，以适应大型人群，以加速数据处理，并加速了科学可视化，从而通过丰富的信息来加速了科学的可视化，并提供了丰富的图像。 此外，重要的是要超越生长轨迹的量化，并将其与作物产量的测量联系起来，以揭示出非常准确的产量代理的测量亚集，以及诊断为显着基因的DNA标记。 这个多学科项目与学生和公众在许多层面的教育密切相关。 研究人员将在与人数不足的群体的才华横溢成员的长期和积极的参与下进行。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。

项目成果

Optimal Path Planning for a Team of Heterogeneous Drones to Monitor Agricultural Fields

异构无人机团队监测农田的最优路径规划

• DOI: 10.1115/dscc2020-3244
• 发表时间: 2020
• 期刊: ASME 2020 Dynamic Systems and Control Conference
• 作者: Faryadi, Saba;Davoodi, Mohammadreza;Mohammadpour Velni, Javad
• 通讯作者: Mohammadpour Velni, Javad