Artificial Intelligence for Arid Land Agriculture (AIALA)

干旱地区农业人工智能（AIALA）

• 批准号: 2151254
• 负责人: Enrico Pontelli
• 金额: $ 200万
• 依托单位: New Mexico State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151254&HistoricalAwards=false
• 关键词: Artificial; Intelligence; Arid; Land; Agriculture

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.

该奖项的全部或部分资金根据《2021 年美国救援计划法案》（公法 117-2）提供。由于世界人口不断增加、气候变化、自然资源减少和有限的资源，全球粮食供应和粮食安全面临风险。可用土地。在农业领域，主要挑战是如何在更少的耕地、更少的水、更少的劳动力和更少的确定性的情况下提高生产力。在干旱地区，这些挑战更加严重。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、人口下降导致的劳动力稀缺、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老化。随着气候变化，覆盖美国西部大部分地区的干旱地区和旱地预计将会扩大。人工智能 (AI) 可以带来范式转变，帮助解决干旱地区农牧业的双重经济和环境挑战。人工智能可以通过自主系统（例如无人机、地面车辆和智能灌溉系统）的协助以及智能软件系统的支持来辅助决策（例如检测和解决农作物病害），从而支持农民更高效、更精确地进行操作）。人工智能驱动的解决方案不仅能让农民事半功倍，还能让农民事半功倍。它们还将提高质量并确保农作物和牲畜更快地进入市场。国家科学基金会向新墨西哥州立大学 (NMSU) 授予的研究实习生 (NRT) 奖将有助于创建一个协调的研究生培训计划，称为干旱土地农业人工智能 (AIALA)，通过以下方式为下一代学者和实践者做好准备：教研究生如何弥合人工智能和干旱地区农业之间的鸿沟。该项目预计培训 33 名硕士和博士。来自计算相关学科和农业相关学科的学生，其中包括18名受资助的学员。AIALA学者的经验将与传统的研究生学科培训相融合和补充，从而为人工智能或农业相关的研究人员提供深入的学科研究地区。此外，这些经验将使学者们能够有效地成为利用人工智能解决干旱土地挑战的研究团队的催化剂。 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发展水平。该研究将促进新型多智能体系统框架的创建，推动机器学习和分布式数据分析的最先进水平。此外，它将提供方法和技术，以增强农作物、牧场植物和牲畜的适应性，提高牲畜在广阔崎岖的牧场中的恢复能力，并最终形成有弹性和可持续的干旱土地农业系统。 AIALA 培训模式受益于多项创新。首先，它建立了一个跨学科的培训渠道，将人工智能研究挑战嵌入干旱地区农业挑战中，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，反过来又得到广泛的指导基础设施的支持。第三，它在所有运作和学习活动中注入多样性和包容性，促进多元化学者的参与，并使学者成为包容性变革的推动者。最后，它强调将专业技能的发展作为整体学科培训的一部分。 NSF 研究实习 (NRT) 计划旨在鼓励为 STEM 研究生教育培训开发和实施大胆的、具有潜在变革性的新模式。该计划致力于通过创新、循证且符合不断变化的劳动力和研究需求的综合培训模式，对高度优先的跨学科或融合研究领域的 STEM 研究生进行有效培训。该奖项反映了 NSF 的法定使命，并被视为值得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来支持。

项目成果

Potential of Accelerometers and GPS Tracking to Remotely Detect Perennial Ryegrass Staggers in Sheep

加速计和 GPS 跟踪远程检测绵羊多年生黑麦草摇晃的潜力

• DOI: 10.1016/j.atech.2022.100040
• 发表时间: 2022-12
• 期刊: Smart Agricultural Technology
• 作者: Trieu, Ly Ly;Bailey, Derek W.;Cao, Huiping;Son, Tran Cao;Scobie, David R.;Trotter, Mark G.;Hume, David E.;Sutherland, B. Lee;Tobin, Colin T.
• 通讯作者: Tobin, Colin T.

Answer Set Planning: A Survey

答案集规划：调查

• DOI: 10.1017/s1471068422000072
• 发表时间: 2022-04
• 期刊: Theory and Practice of Logic Programming
• 影响因子: 1.4
• 作者: CAO TRAN, SON;PONTELLI, ENRICO;BALDUCCINI, MARCELLO;SCHAUB, TORSTEN
• 通讯作者: SCHAUB, TORSTEN

A Case Study Using Accelerometers to Identify Illness in Ewes following Unintentional Exposure to Mold-Contaminated Feed

使用加速度计识别无意接触霉菌污染饲料的母羊疾病的案例研究

• DOI: 10.3390/ani12030266
• 发表时间: 2022-01-21
• 期刊: Animals
• 影响因子: 3
• 作者: Gurule SC;Flores VV;Forrest KK;Gifford CA;Wenzel JC;Tobin CT;Bailey DW;Hernandez Gifford JA
• 通讯作者: Hernandez Gifford JA