NSF Convergence Accelerator Track J Phase 2: CropSmart - a digital twin for making wiser cropping decisions nationwide
NSF 融合加速器轨道 J 第 2 阶段:CropSmart - 用于在全国范围内做出更明智的种植决策的数字孪生
基本信息
- 批准号:2345039
- 负责人:
- 金额:$ 500万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Cooperative Agreement
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-12-15 至 2026-11-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Healthy crop production in the U.S. is critical for not only the food and nutrition security of the U.S. and the world but also the prosperity of the U.S. economy. The USDA Agricultural Innovation Agenda calls for increasing U.S. agricultural production by 40% while cutting its environmental footprint in half by 2050. Sound crop management decision-making is a key to achieving this ambitious goal. An example of such decision-making is “should I irrigate my cornfield today? If so, by how many inches of water?” Traditionally, such decisions are made by individuals based on their empirical judgment, which is often subjective and less optimal. Science-based, data-driven approaches for cropping decision-making rely on timely and accurate information on current and predicted future conditions of crop and environment to make optimal decisions. However, it remains a challenge for stakeholders to adopt the data-driven approach because they do not have full and effective access to the timely and accurate information and lack facilities or knowledge to process the information. This project will meet the challenge by offering the data-driven optimal cropping decision-making services nationwide up to field scales through developing and operating the CropSmart digital twin. The services will be accessible to users through both web portals and smartphone Apps. This project will help USDA to archive its innovation goal, enhance food and nutrition security of the U.S. and the world, and bring hundred-million-dollar economic return and huge environmental benefits to U.S. economy and society annually.CropSmart, to be built and operated by this project, is a digital replica of real-world cropping systems over the contiguous US up to 10-m spatial resolution. It will not only accurately represent the current crop and environment conditions, but also predict, with acceptable confidence levels, future conditions with hypothetical “what if” scenarios, resulting in actionable predictions. CropSmart will provide three services to users: 1) user-specific decision ready information on which the user can make data-driven decision; 2) “what if” tradeoff service which will generate consequences (e.g., yield, economic return, or environmental footprint) of different user decision options so that the user can find the optimal decision; and 3) decision advice service which will automatically generate optimal decision based on a user’s decision goal. CropSmart will be built by integrating the advanced remote sensing, crop and environmental modeling, AI/ML, agro-geoinformatics, and digital twin technologies through the multi-disciplinary convergence approach. The major project activities will include: 1) implementing CropSmart to support at least 6 types of top-priority decision-making use-cases specified by the user community; (2) deploying CropSmart operationally to cultivate its user community and show its gaming-change impacts; 3) broadening adoption, participation, and impact through a comprehensive education, extension, and outreach program; and (4) establishing a community-based CropSmart.org and implement the sustainability plan to sustain CropSmart activities after project expires and maximize the long-term project impacts. At the end of the performance period, this project will deliver the CropSmart software package, the operational CropSmart services, and a sustained community of at least 6,000 users.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.
美国的健康作物生产不仅对美国和世界的粮食和营养安全至关重要,而且对美国经济的繁荣至关重要。美国农业部农业创新议程呼吁将美国农业产量提高 40%,同时减少其环境足迹。到 2050 年,这一数字将减半。健全的作物管理决策是实现这一宏伟目标的关键。此类决策的一个例子是“如果是的话,我今天应该灌溉玉米地吗?”传统上,此类决定是由个人根据经验判断做出的,这通常是主观的,基于科学的、数据驱动的种植决策方法依赖于及时、准确的当前信息。并预测作物和环境的未来状况以做出最佳决策。然而,利益相关者采用数据驱动的方法仍然是一个挑战,因为他们无法充分有效地获取及时、准确的信息,并且缺乏处理设施或知识。该项目将通过提供以下信息来应对挑战。通过开发和运营 CropSmart 数字双胞胎,在全国范围内提供数据驱动的最佳种植决策服务。用户可以通过门户网站和智能手机应用程序访问该服务,该项目将帮助美国农业部实现其创新目标,增强其创新能力。保障美国乃至世界的粮食和营养安全,每年为美国经济和社会带来数亿美元的经济回报和巨大的环境效益。该项目将建设和运营的CropSmart是现实世界的数字复制品它不仅可以准确地表示当前的作物和环境状况,还可以通过假设的“假设”场景以可接受的置信水平预测未来状况,从而产生可行的预测。 CropSmart 将为用户提供三项服务:1)用户特定的决策准备信息,用户可以根据该信息做出数据驱动的决策;2)“假设”权衡服务,该服务将产生后果(例如产量、经济回报或环境) 3)决策建议服务,将通过集成先进的遥感、作物和环境,根据用户的决策目标自动生成最佳决策。通过多学科融合方法,建模、人工智能/机器学习、农业地理信息学和数字孪生技术主要项目活动将包括:1) 实施 CropSmart 以支持至少 6 类最优先的决策。用户社区指定的用例;(2) 在运营上部署 CropSmart,以培养其用户社区并展示其游戏变革影响;3) 通过全面的教育、推广和外展计划扩大采用、参与和影响; 4) 建立一个以社区为基础的CropSmart.org并实施可持续发展计划,以在项目结束后维持CropSmart活动并最大化项目的长期影响。在执行期结束时,该项目将交付CropSmart软件包、运营软件。 CropSmart 服务,以及至少 6,000 名用户的持续社区。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,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
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专利数量(0)
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Liping Di其他文献
Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques
利用本体工程和网络基础设施技术实现遥感影像无参数自动分类
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:4.4
- 作者:
Ziheng Sun;Hui Fang;Liping Di;Peng Yue - 通讯作者:
Peng Yue
CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support
CropScape:基于 Web 服务的应用程序,用于探索和传播美国连续地理空间农田数据产品以提供决策支持
- DOI:
10.1016/j.compag.2012.03.005 - 发表时间:
2012-06-01 - 期刊:
- 影响因子:8.3
- 作者:
Weiguo Han;Zhengwei Yang;Liping Di;Richard Mueller - 通讯作者:
Richard Mueller
Adding Geospatial Data Provenance into SDI—A Service-Oriented Approach
将地理空间数据来源添加到 SDI 中——面向服务的方法
- DOI:
10.1109/jstars.2014.2340737 - 发表时间:
2014 - 期刊:
- 影响因子:0
- 作者:
Lianlian He; Peng Yue;Liping Di;Mingda Zhang - 通讯作者:
Mingda Zhang
Geoscience data provenance: an overview
地球科学数据来源:概述
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:8.2
- 作者:
Liping Di;Peng Yue;Hampapuram K. Ramapriyan;Roger L. King - 通讯作者:
Roger L. King
Cyberinformatics tool for in-season crop-specific land cover monitoring: Design, implementation, and applications of iCrop
- DOI:
10.1016/j.compag.2023.108199 - 发表时间:
2023-10-01 - 期刊:
- 影响因子:0
- 作者:
Chen Zhang;Liping Di;Li Lin;Haoteng Zhao;Hui Li;Anna Yang;Liying Guo;Zhengwei Yang - 通讯作者:
Zhengwei Yang
Liping Di的其他文献
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{{ truncateString('Liping Di', 18)}}的其他基金
NSF Convergence Accelerator Track J: Building a digital twin for national-scale field-level crop monitoring, prediction, and decision support
NSF 融合加速器轨道 J:为国家规模的田间作物监测、预测和决策支持构建数字孪生
- 批准号:
2236137 - 财政年份:2022
- 资助金额:
$ 500万 - 项目类别:
Standard Grant
EAGER: Collaborative Research: Spatiotemporal transfer learning for enabling cross-country and cross-hemisphere in-season crop mapping
EAGER:协作研究:时空迁移学习,用于实现跨国和跨半球的当季作物绘图
- 批准号:
2228000 - 财政年份:2022
- 资助金额:
$ 500万 - 项目类别:
Standard Grant
EAGER: Collaborative Research: Spatiotemporal transfer learning for enabling cross-country and cross-hemisphere in-season crop mapping
EAGER:协作研究:时空迁移学习,用于实现跨国和跨半球的当季作物绘图
- 批准号:
2228000 - 财政年份:2022
- 资助金额:
$ 500万 - 项目类别:
Standard Grant
INFEW/T2:WaterSmart: A Cyberinfrastructure-Based Integrated Agro-Geoinformatic Decision-Support Web Service System to Facilitate Informed Irrigation Decision-Making
INFEW/T2:WaterSmart:基于网络基础设施的综合农业地理信息决策支持网络服务系统,促进知情灌溉决策
- 批准号:
1739705 - 财政年份:2017
- 资助金额:
$ 500万 - 项目类别:
Standard Grant
EarthCube Integration: CyberWay--Integrated Capabilities of EarthCube Building Blocks for Facilitating Cyber-based Innovative Way of Interdisciplinary Geoscience Studies
EarthCube集成:CyberWay——EarthCube构建模块的集成能力,促进基于网络的跨学科地球科学研究创新方式
- 批准号:
1740693 - 财政年份:2017
- 资助金额:
$ 500万 - 项目类别:
Standard Grant
EarthCube Building Blocks: CyberConnector: Bridging the Earth Observations and Earth Science Modeling for Supporting Model Validation, Verification, and Inter-comparison
EarthCube 构建模块:CyberConnector:连接地球观测和地球科学建模以支持模型验证、验证和相互比较
- 批准号:
1440294 - 财政年份:2014
- 资助金额:
$ 500万 - 项目类别:
Standard Grant
EarthCube Domain End-User Workshop: Engaging the Atmospheric Cloud/Aerosol/Composition Community
EarthCube 域最终用户研讨会:参与大气云/气溶胶/成分社区
- 批准号:
1342148 - 财政年份:2013
- 资助金额:
$ 500万 - 项目类别:
Standard Grant
EAGER: Collaborative Research: Interoperability Testbed-Assessing a Layered Architecture for Integration of Existing Capabilities
EAGER:协作研究:互操作性测试台 - 评估用于集成现有功能的分层架构
- 批准号:
1239615 - 财政年份:2012
- 资助金额:
$ 500万 - 项目类别:
Standard Grant
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