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“增长融合研究”项目旨在通过生命科学（植物生物学、农学、遗传学）、物理科学（工程学）和计算科学的深度整合来改良作物。 具体目标是开发工具和应用方法，利用这些各自领域的进步来加速作物改良进程——这是一个具有全球重要性的成果。提案者寻求在植物结构测量方面的早期进展的基础上再接再厉，扩大规模以适应大量种群，加速数据处理和增强科学可视化，以有效地将图像与后处理算法提供的丰富信息配对。 此外，重要的是超越生长轨迹的量化，并将其与作物产量的测量联系起来，以揭示准确代表产量的测量子集，以及诊断显着基因的 DNA 标记。 这个多学科项目与多个层面的学生和公众教育密切相关。 研究人员将在长期积极参与代表性不足群体的优秀成员的基础上。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。

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