Source: WASHINGTON STATE UNIVERSITY submitted to
AI INSTITUTE: AGRICULTURAL AI FOR TRANSFORMING WORKFORCE AND DECISION SUPPORT (AGAID)
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
NEW
Funding Source
Reporting Frequency
Annual
Accession No.
1027037
Grant No.
2021-67021-35344
Project No.
WNP00874
Proposal No.
2021-07245
Multistate No.
(N/A)
Program Code
A7303
Project Start Date
Sep 1, 2021
Project End Date
Aug 31, 2022
Grant Year
2021
Project Director
Kalyanaraman, A.
Recipient Organization
WASHINGTON STATE UNIVERSITY
240 FRENCH ADMINISTRATION BLDG
PULLMAN,WA 99164-0001
Performing Department
School of EECS
Non Technical Summary
A growing world population will increase food demand while at the same time, agriculture faces complex challenges related to labor, water scarcity, weather events and climate change. The AgAID Institute will develop artificial intelligence (AI) solutions to help address these pressing challenges and spur the next agricultural revolution with the use of AI. More specifically, the Institute will build tools and workflows to help mitigate the effects of labor costs and shortages, and better manage regional resources such as water, despite climate uncertainties. The Institute will bring more data and science-guided information to the fingertips of agricultural workers to help them make better decisions.The Institute will emphasize solutions that can adapt to changing environments and climate, and amplify productivity through more efficient human and machine partnerships. Most importantly, however, the Institute's vision will be realized by making AI adoption its distinctive first principle. This means that that the people who will use the tools--the farmers, workers, and managers--will be deeply involved throughout all stages of the development process. The goal is to ensure the AI solutions are practical and add value, making them more likely to be used in dynamic real-world situations.Education and outreach are central to AgAID's activities not just to encourage AI adoption but also as a matter of justice. Raising AI skill levels and opening new career paths will increase compensation and improve quality of life for the agricultural workforce while attracting more people to agriculture and computing professions. This will be accomplished through K-12, college, and workforce training efforts carried out by the Institute's multi-disciplinary core members that include eight academic institutions and two tech companies as well as a range of public and private sector stakeholder groups. Partnerships with minority-serving institutions will engage Hispanic and Native American students. The Institute will work to develop inclusive AI interactions, such as bilingual and intuitive applications, to respond to the needs of a diverse workforce. By increasing AI education and closing skill gaps, the Institute aims to help transform this critical labor force and create new opportunities for computing and STEM majors.AgAID Institute's test cases will involve specialty crops such as apples, grapes, mint, and almonds that are grown nationwide and particularly in the Western U.S. These crops pose several agricultural grand challenges: they require intensive labor, need irrigation, and are heavily impacted by weather events and climate change. Specialty crops also account for 87% of the U.S. agricultural workforce. About 40% of these crops are perennial, requiring long-term management and planning. AI-based solutions that overcome these challenges for specialty crops will be more readily transferrable to other regions across the country and globally.If successful, the AgAID Institute's work will lead directly to the launch of several AI-powered systems used in agriculture in the near future. More importantly, the experience of successfully moving AI from the lab to operations will form a blueprint for rapidly addressing new agricultural challenges with AI. In this way, the AgAID Institute will provide long-term leadership in generating practical, real world solutions to address the complex web of challenges presented by a growing population and a changing climate.
Animal Health Component
0%
Research Effort Categories
Basic
30%
Applied
30%
Developmental
40%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
1110210205010%
1110210207010%
2041119208010%
4021119208010%
4021219208010%
6010210208010%
8031119208010%
8031219208010%
9030210208010%
9030430208010%
Goals / Objectives
The overarching goals of the AgAID institute are (a) to bring about a fundamental transformation in Ag decision support, farm operations and workforce development, using foundational developments in AI, and (b) to build a new coalition to create inclusive AI-Ag that is prepared and ready to take on future challenges of societal importance.Our institute's activities will be coordinated along nine interwoven dimensions or "thrusts" which include:Ag Thrusts:Water allocations intelligenceFarm operations intelligenceLabor intelligenceAI Thrusts:Modeling systems of knowns and unknownsMulti-scale decision supportInteractive human-AI workflowsDEMO FarmBroader Impact Thrusts:Adoption and technology transferEducation, extension and workforce developmentBroadening participationResearch thrusts and objectives:Ag Thrust 1. Water allocation intelligenceHypothesis: Addressing water-scarcity challenges will require new AI-enabled models of the coupled human and natural system, and AI-enabled decision support.Key objectives:WAI.obj1) To push the frontiers of hydrologic sciences by incorporating human-water nexus;WAI.obj2) To facilitate a shift from water "supply" to "availability" forecasts;WAI.obj3) To develop tools for optimal water allocation decisions..Use-cases:Irrigation schedulingPlantingInfrastructure planningAg Thrust 2. Farm operations intelligenceHypothesis: AI-enabled real-time, site-specific decision making can optimize farm resources while mitigating crop losses and improving produce quality.Key objectives:FOI.obj1) To develop site-specific models connecting accumulated management decisions to seasonal crop yield and quality outcomes;FOI.obj2) To construct a sensor-driven, adaptive real-time farm operations decision support framework.Use-cases:Frost mitigationDeficit irrigationHarvest managementAg Thrust 3. Labor intelligenceHypothesis: Challenges posed by increasing labor costs and a shortage in skilled labor workforce can be effectively addressed through human-machine partnerships.Key objectives:LI.obj1) To improve efficiency of existing field machines with AI;LI.obj2) To augment a less experienced workforce with intelligent machines;LI.obj3) To amplify more-experienced workers' productivity by training machines.Use-cases:Mechanical harvestingFlower thinningIntelligent pruningAI Thrust 1: Modeling Systems of Knowns and UnknownsKey objectives: AI.1.obj1) To design approaches to integrate simulators, data, and unknowns;AI.1.obj2) To design approaches for guided data collection and monitoring.AI Thrust 2: Multi-scale Decision SupportAI.2.obj1) To design approaches for site-specific real-time decision support;AI.2.obj2) To design approaches for long-horizon decision support.AI Thrust 3: Design of Interactive and Inclusive Human-AI WorkflowsAI.3.obj1) To design human-AI workflows for iterative human-centered design;AI.3.obj2) To design approaches for After-Action Review for AI;AI.3.obj3) To design approaches for unobtrusive instrumentation;AI.3.obj4) To design approaches for in-situ utility elicitation;AI.3.obj5) To design explainable interfaces;AI3.obj6) To design human-AI workflows with inclusive design.DEMO Farm (crosscutting research and broader impact)Key objectives:DEMO.obj1) To establish an interactive research platform for transdisciplinary teams to work side-by-side to better understand how AI technologies should be developed for different agricultural use cases;DEMO.obj2) To create an educational hub for students to gain hands-on experience;DEMO.obj3) To create an experiential learning site for key stakeholders including growers, field workers, educators, and technology providers.Broader impact thrusts and objectives:Adoption and technology transferKey objectives:TAT.obj1) To develop an ecosystem of intermediaries at the early stages of AgAID research and development to accelerate innovation feedback cycles and to facilitate technology adoption and transfer.TAT.obj1) To facilitate the creation of early adopter networks;TAT.obj2) To help create commercialization partners as part of an adoption flywheel;TAT.obj3) To identify areas where third party adoption could benefit user communities;TAT.obj4) To characterize how financial intermediation could support farmers at different scales.Education, extension and workforce developmentKey objectives:Ext.obj1) To facilitate a needs-driven co-development of inclusive and responsible AI tools via learning circles, resulting in tools with higher adoption likelihood, more adaptive to changing requirements, and better trust in AI for Ag participants;Ext.obj2) To jumpstart the adoption process by leveraging stakeholder relationships to recruit an early adopters' network;Ext.obj3) To support adoption amplification into user communities through training programs, and engagement with intermediaries to scale tech transfer and adoption.Ed.obj1) To increase STEM awareness at K-12 level, with a focus on middle and high school levels when students identify career preferences and often opt-out of STEM;Ed.obj2) To provide STEM retention pathways to students in junior colleges and 2-year associate degree programs which offer vital stepping stones to 4-year colleges, especially for lower income, underrepresented, and first generation students;Ed.obj3) To provide hands-on research experiences to undergraduate students for enriching their learning, influencing career choices and building occupational identity;Ed.obj4) To train and prepare graduate students and postdoctoral scholars who will form the next-generation workforce in research and technology development.Broadening participationKey objectives:BP.obj1) To explicitly engage Hispanic, Native American, and Women participants in all phases of AgAID implementation through an organizational cycle of identification, recruitment, training, practice, refinement, retention, and promotion;BP.obj2) To empower AgAID participants to engage in ideation, discovery, and implementation, while actively identifying and removing social barriers and biases;BP.obj3) To continuously assess and refine AgAID Diversity-Equity-Inclusion (DEI) activities at all educational levels (K-14, UG, G, PD), and to advance DEI within Ag-Tech workforce.
Project Methods
Approach:Tackling the grand challenges of 21st century agriculture will require fundamental shifts in the way we envision the role of AI technologies, and in the way we build AI systems. This shift is especially true for complex agricultural ecosystems such as in the Western U.S. The traditional approach toward development and deployment has been to view AI and technology designers as solution providers and the domain users as consumers. This monolithic view of producers and consumers, however, becomes grossly inadequate when brought to the fore in agriculture, which is a complex commercial multi-crop enterprise involving multiple stakeholders including the farmers (growers), farm laborers, consultants and technology service providers, state and regional policy makers, researchers and extension scientists, and students who form the future workforce. Therefore, for any AI-driven endeavor to succeed in this complex "Ag-sphere" there must be a strong alliance built between the AI designers and this broad range of stakeholders. Secondly, this AI designer - Ag stakeholder (people) alliance needs to be complemented by a strong AI technology - human factors (system) alliance - i.e., AI capabilities will need to include an inherent ability to integrate human input and account for human behavior. Humans can provide expert (scientific or in-field) guidance or be influential actors in complex dynamic processes (e.g. water use). Clearly, forging these two dimensions of alliances, people and system, will be beyond the scope of any specific research project or disciplinary silo, and warrants a transdisciplinary multi-party institute-scale effort - one that can propel AI developments throughout the Ag industry.To realize this vision, the AgAID institute will be built on the foundations of our partnerships between the team and our stakeholder groups, with AI, Ag, and humans as its three major intellectual pillars, and guided by three unifying principles that can be succinctly summarized as "Adopt-Adapt-Amplify". More specifically, we consider adoption as a first principle in AI design, adaptability to changing environments and scales, and amplification of human skills and machine efficiency - to be three important cross-cutting principles of design that will guide our approach to the core activities of our institute.Adoption as a first principle in AI design is removing barriers to AI technology adoption in Ag applications. This will be accomplished by: a) treating practical constraints and user considerations as central to the AI design process; and b) creating an environment of technology and knowledge co-production via proactive and continuous bidirectional engagement with the stakeholders.Adaptability to changing environments and scales is an ability that our approaches will inherently encode - to address the impacts of climate variability and weather fluctuations on agricultural productivity, and to provide decision support at multiple spatiotemporal scales of the Ag-sphere.Amplifying human skills and machine efficiency by augmenting automation with human skills and creating a close human-AI partnership will be critical to closing the gaps in workforce, while ensuring behavioral consistency and reduced uncertainty in decision support. Amplification will both enhance human skills and knowledge and improve machine efficiency, leading to a whole that is greater than the sum of its parts.Efforts:The AgAID Institute's activities will be coordinated along nine interwoven dimensions or "thrusts" that fall under three broad categories:[Ag thrusts]Water allocation intelligenceFarm operations intelligenceLabor intelligence[AI thrusts]Modeling systems of knowns and unknownsMulti-scale decision supportInteractive human-AI workflows[Broader impact thrusts]Adoption and technology transferEducation, extension and workforce developmentBroadening participationThe thrusts are structured in a way to create maximum synergy between the agricultural objectives through AI foundational advances. There are also strong ties between research and broader impact activities, as workforce training and stakeholder engagement are embedded within our research activities.Efforts will include (but not limited to): research training, research collaboration, extension and outreach, demonstration sessions and field trips, formal classroom instruction, laboratory instruction, practicum/internship experiences, certifications, and workshops and other experiential learning opportunities, and partnerships and nexus activities.Evaluation:Kansas State University's Office of Educational Innovation and Evaluation (OEIE) will serve as the AgAID evaluator. OEIE has extensive expertise in evaluation design, education research, quantitative and qualitative evaluation methods, robust data collection/analysis, and reporting, and has provided external evaluation for eighty-five NSF-funded projects. OEIE adheres to the American Evaluation Association's Guiding Principles for Evaluators and the Program Evaluation Standards of the Joint Committee on Standards for Educational Evaluation. The evaluation plan, includes formative evaluation to support project management and enable mid-course alterations, and summative evaluation to assess and document the overall impacts and outcomes. Evaluation activities are designed to gather data from the broad range of institute participants, and draw on both qualitative and quantitative methodologies and triangulate data when possible for more robust findings. OEIE will collaborate with the management team to finalize project milestones, progress metrics, and evaluation activities (e.g. surveys, interviews, and focus groups). Summaries will be submitted to project leadership to provide on-going feedback on project activities, integration of project components, and suggestions to maximize project success.