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到2050年将美国农业产量增加40%，同时将其环境足迹减少一半。合理的作物管理决策是实现这一宏伟目标的关键。这种决策的一个例子是“我今天应该灌溉我的玉米地吗？”如果是这样，有多少英寸的水？”传统上，这些决定是由个人根据他们的经验判断做出的，这往往是主观的，不太理想的。以科学为基础、数据驱动的种植决策方法依赖于及时准确的作物和环境当前和预测未来状况的信息，以做出最佳决策。然而，利益攸关方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得及时和准确的信息，缺乏处理信息的设施或知识。该项目将通过开发和运营CropSmart数字孪生模型，在全国范围内提供数据驱动的最佳种植决策服务，以应对这一挑战。用户可通过门户网站和智能手机应用程序访问这些服务。该项目将帮助美国农业部实现其创新目标，提高美国和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。CropSmart是一个空间分辨率高达10米的美国本土真实世界种植系统的数字复制品。它不仅能准确地反映当前的作物和环境状况，而且还能以可接受的置信水平预测未来的状况，并提出假设性的“假设”方案，从而得出可操作的预测。CropSmart将为用户提供三种服务：1）用户可以做出数据驱动决策的用户特定决策就绪信息; 2）将产生后果的“如果”权衡服务（例如，产量、经济回报或环境足迹），以便用户可以找到最佳决策;以及3）决策建议服务，其将基于用户的决策目标自动生成最佳决策。CropSmart将通过多学科融合方法集成先进的遥感，作物和环境建模，AI/ML，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，以支持用户社区指定的至少6种最优先决策用例; 2）在运营中部署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，推广和推广计划扩大采用，参与和影响。及（4）建立一个以社区为基础的网站CropSmart.org，并实施可持续发展计划，以在项目到期后维持CropSmart活动，并最大限度地发挥项目的长期影响。在执行期结束时，该项目将交付CropSmart软件包、可操作的CropSmart服务和至少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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。