AccelNet: Accelerate Integration of Engineering and Agricultural Research using Artificial Intelligence (AI2EAR)

粮食生产是一项复杂的全球性事业。 科学差距、技术差距和社会障碍阻碍了满足全球粮食安全需求的能力。 这一催化级的人工智能网络项目（利用人工智能加速工程和农业研究的整合-AI 2NET）将促进提高产量、减少作物损失、减少作物资源需求和增加食物营养的战略。 AI 2AI将在传感器科学、多尺度建模和数据分析（特别是通过人工智能）方面建立国际合作努力的联系。新兴的网络网络将使研究人员能够更好地应对与农业中看到和预期的环境，生态和生物变异性相关的挑战。AI 2 EE提供了一种参与者网络，利用负责任的研究和创新以及利益相关者持续参与的原则来改变，塑造和影响21世纪世纪农业预期的创新路径。AI 2AI将解决粮食安全面临的主要挑战：1）无法感知、量化和预测环境中的物理和化学变量，包括微生物组; 2）缺乏将并发多尺度数据集转换为决策策略的数据分析和模型; 3）缺乏开源网络基础设施，无法访问、分析和分享从生物、农业和环境数据集中提取的见解。 合作学习，农场技术培训，合作推广以及学生和博士后学者的国际交流机会将为该领域未来的跨学科领导做好准备。人工智能2009将开发和传播产品和做法，以整合和转让知识。作为网络之网的一部分而发展的互动，包括来自人工智能2008研讨会和参与调查的互动，将加速共享和采用迫切需要的技术，以促进下一代精密农业解决方案的创新。通过国际网络对网络合作加速研究该计划旨在加速科学发现的进程，并为下一代美国研究人员进行多团队国际合作做好准备。 该计划支持美国研究网络和海外互补网络之间的战略联系，这些网络将利用研究和教育资源来应对需要重大协调国际努力的重大科学挑战。该奖项由生物科学理事会的生理机制和生物力学计划以及植物基因组研究计划共同资助。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。

Food production is a complex global enterprise. Scientific gaps, technical gaps, and social barriers hinder the ability to meet global food security needs. This catalytic-level AccelNet project (Accelerate Integration of Engineering and Agricultural Research using Artificial Intelligence – AI2EAR) will promote strategies to improve yield, reduce crop loss, decrease crop resource demands, and increase food nutrition. AI2EAR will create links across international collaborative efforts focused on sensor science, multiscale modeling, and data analytics, particularly through artificial intelligence. The emerging network of networks will enable researchers to better address challenges associated with environmental, ecological, and biological variabilities seen and expected in agriculture. AI2EAR provides a means participant networks to use principles of responsible research and innovation and continual stakeholder engagement to change, shape, and influence the path of innovation expected in 21st century agriculture. AI2EAR will address key challenges to food security: 1) the inability to sense, quantify, and predict physical and chemical variables in the environment, including the microbiome; 2) the lack of data analytics and models that transform concurrent multi-scale datasets into decision-making strategies; and 3) the lack of open-source cyber-infrastructures for accessing, analyzing, and sharing the insights extracted from biological, agricultural, and environmental datasets. Collaborative learning, on-farm technology training, cooperative extension, and international exchange opportunities for students and postdoctoral scholars will prepare future transdisciplinary leads in this field. AI2EAR will develop and disseminate products and practices to integrate and transfer knowledge. Interactions developed as part of the network of networks, including from AI2EAR workshops and survey participation, will accelerate sharing and adoption of technologies urgently needed to facilitate innovation for next-generation precision-ag solutions.The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts. Co-funding for this award is provided by the Physiological Mechanisms and Biomechanics Program and the Plant Genome Research Program in the Directorate for Biological Sciences.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.