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

• 批准号: 2345039
• 负责人: Liping Di
• 金额: $ 500万
• 依托单位: George Mason University
• 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: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345039&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Phase

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.

美国健康的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也至关重要。美国农业部的农业创新议程要求将美国农业产量增加40％，同时将其环境足迹截至2050年。声音作物管理决策是实现这一雄心勃勃的目标的关键。这种决策的一个例子是“我今天应该灌溉玉米田吗？如果是这样，几英寸的水？”传统上，这样的决定是由个人根据其经验法官做出的，该决定通常是主观的且最佳的。基于科学的，以数据为基础的作物决策方法依赖于有关当前和预测的作物和环境条件的及时，准确的信息来做出最佳决策。但是，利益相关者采用数据驱动方法仍然是一个挑战，因为他们无法完全有效地访问及时，准确的信息，并且缺乏处理信息的设施或知识。该项目将通过在全国范围内提供数据驱动的最佳裁剪决策服务来应对挑战，直到田间尺度来开发和运营农作物数字双胞胎。用户将通过Web门户和智能手机应用程序访问该服务。该项目将帮助USDA归档其创新目标，增强美国和全世界的食品和营养安全，并每年为美国经济和社会带来数十亿美元的经济回报，并为美国经济和社会带来巨大的环境收益。该项目将由该项目建立和运营，是由现实世界中的数字复制品建立和运营，而不是现实世界中的数字复制品，而不是现实世界中的偏见。它不仅可以准确地代表当前的作物和环境状况，而且还可以预测，具有可接受的置信度，未来的条件，假设“如果”场景，导致了可行的预测。农作物将为用户提供三种服务：1）用户特定的决策准备信息，用户可以在其中做出数据驱动的决策； 2）“如果”权衡服务，将产生不同用户决策选项的后果（例如，收益，经济回报或环境足迹），以便用户可以找到最佳决定； 3）决策咨询服务农作物将通过整合高级遥感，作物和环境建模，AI/ML，Agro-GeoInformatics和数字双胞胎技术来建立。主要项目活动将包括：1）实施农作物以支持用户社区指定的至少6种高优先级决策用例； （2）在操作上部署农作物以培养其用户社区并显示其游戏变化的影响； 3）通过全面的教育，扩展和外展计划扩大采用，参与和影响； （4）建立一个基于社区的农作物Mart.org并实施可持续性计划，以在项目到期后维持农作物活动并最大程度地发挥长期项目影响。在绩效期结束时，该项目将提供农作物软件包，运营农作物服务以及至少有6,000名用户的持续社区。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准通过评估来获得的支持。