Artificial Intelligence for Arid Land Agriculture (AIALA)

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。由于世界人口增长、气候变化、自然资源减少和可用土地有限，全球粮食供应和粮食安全面临风险。在农业方面，主要的挑战是如何用更少的耕地、更少的水、更少的劳动力和更少的确定性来提高生产力。在干旱地区，这些挑战更加严峻。农业系统难以应对水资源供应和土地利用模式的快速变化、人口减少导致的劳动力短缺、天气和气候变化带来的可变性和不确定性，以及农村基础设施的老化。随着气候变化，覆盖美国西部大部分地区的干旱地区预计将扩大。人工智能（AI）可以带来范式转变，解决干旱地区农业和牧场的双重经济和环境挑战。人工智能可以通过自主系统（如无人机、地面车辆和智能灌溉系统）和智能软件系统的支持来帮助决策（如检测和解决作物病害），帮助农民提高效率和精度。人工智能驱动的解决方案不仅能让农民事半功倍；它们还将提高质量，确保作物和牲畜更快进入市场。这项授予新墨西哥州立大学（NMSU）的国家科学基金会研究培训（NRT）奖将创建一个名为“干旱地区农业人工智能”（AIALA）的协调研究生培训计划，通过教授研究生如何弥合人工智能与干旱地区农业之间的鸿沟，为下一代学者和实践者做好准备。该项目预计将培养33名来自计算机相关学科和农业相关学科的硕士和博士研究生，其中包括18名受资助的学员。AIALA的学者经验将与传统的研究生学科培训相结合和补充，从而为人工智能或农业相关领域的研究人员提供深入的学科研究背景。此外，这一经验将使学者能够有效地在利用人工智能解决干旱土地挑战的研究团队中发挥催化剂作用。AIALA学者及其研究导师进行的研究将推动人工智能和干旱地区农业的最新发展。该研究将促进新的多智能体系统框架的创建，推进机器学习和分布式数据分析的最新技术。此外，它将提供方法和技术，以增强作物、牧场植物和牲畜的适应性，提高牲畜在广阔崎岖的牧场上的适应能力，并最终建立有适应能力和可持续的干旱土地农业系统。AIALA的培训模式得益于许多创新。首先，它建立了一个跨学科的培训管道，将人工智能研究挑战嵌入干旱地区农业挑战，实现情境化和情境化学习。其次，它将研究生和教师导师整合到相互支持的学习者团队中，反过来，由广泛的指导基础设施提供支持。第三，它在所有的运营和学习活动中注入了多样性和包容性，促进了不同学者群体的参与，并使学者们做好准备，成为包容性变革的推动者。最后，它强调专业技能的发展作为整体学科训练的一部分。美国国家科学基金会研究实习生（NRT）计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。该项目致力于通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式，在高优先级跨学科或融合研究领域对STEM研究生进行有效培训。该奖项反映了美国国家科学基金会的法定使命，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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Global food supply and food security are at risk due to an increasing world population, climate change, diminishing natural resources, and limited available land. In agriculture, the primary challenge has been how to be more productive with less - less arable land, less water, less labor, less certainty. In arid lands, these challenges are amplified. Agricultural systems struggle to cope with rapid changes in water availability and land-use patterns, scarcity of labor due to declining population, variability and uncertainty related to changing weather and climate, and aging rural infrastructures. Arid lands and drylands, which cover much of the Western US, are expected to expand as the climate changes. Artificial intelligence (AI) can bring a paradigm shift in how the twin economic and environmental challenges of farming and ranching in arid lands can be addressed. AI can support farmers to operate with greater efficiency and precision through the assistance of autonomous systems (e.g., drones, ground vehicles, and intelligent irrigation systems) and the support of intelligent software systems to aid in decision making (e.g., detecting and resolving crop diseases). AI-driven solutions will not only enable farmers to do more with less; they will also improve quality and ensure a faster path-to-market for crops and livestock. This National Science Foundation Research Traineeship (NRT) award to New Mexico State University (NMSU) will enable the creation of a coordinated graduate training program, called Artificial Intelligence for Arid Land Agriculture (AIALA), to prepare the next generation of scholars and practitioners by teaching graduate students how to bridge the divides between AI and Agriculture for Arid Lands. The project anticipates training 33 MS and Ph.D. students, including 18 funded trainees, from computing-related disciplines and agriculture-related disciplines .The AIALA scholar experience will integrate with and complement the traditional graduate disciplinary training, thus contextualizing the in-depth disciplinary research for researchers in either AI or agriculture-related areas. Moreover, the experience will allow scholars to effectively serve as catalysts in research teams using AI to solve arid land challenges. The research conducted by the AIALA scholars and their research mentors will advance the state of the art in both AI and Arid Land Agriculture. The research will promote the creation of novel multi-agent systems frameworks, advancing the state of the art in machine learning and distributed data analytics. In addition, it will provide methodologies and technologies to enhance the adaptability of crops, rangeland plants and livestock, improve the resiliency of livestock in expansive rugged rangelands, and ultimately lead to resilient and sustainable arid land agricultural systems. The AIALA training model benefits from a number of innovations. First, it establishes a transdisciplinary training pipeline, embedding AI research challenges in Arid Land Agricultural challenges, enabling contextualized and situated learning. Second, it integrates graduate students and faculty mentors in mutually supportive teams of learners, supported, in turn, by an extensive mentoring infrastructure. Third, it infuses diversity and inclusion in all operations and learning activities, promoting engagement of a diverse audience of scholars and preparing the scholars to serve as agents of change for inclusion. Finally, it emphasizes the development of professional skills as part of holistic disciplinary training.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.