Source: TEXAS A&M UNIVERSITY submitted to NRP
ARTIFICIAL INTELLIGENCE(AI) IN AGRICULTURE AND NATURAL RESOURCES: INNOVATION TO MANAGE SUSTAINABILITY IN A NEW WORLD OF ENVIRONMENTAL STRESS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1032020
Grant No.
2024-67021-42173
Cumulative Award Amt.
$49,838.00
Proposal No.
2023-11937
Multistate No.
(N/A)
Project Start Date
Apr 15, 2024
Project End Date
Apr 14, 2025
Grant Year
2024
Program Code
[A1521]- Agricultural Engineering
Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001
Performing Department
(N/A)
Non Technical Summary
A conference on artificial intelligence (AI) in agriculture will occur April 15-17, 2024 inCollege Station, Texas. The conference will focus on AI for "Innovation and Discovery to Manage Sustainability in a New World of Environmental Stress", following two successful AI conferencesfocused on other topics. We seek to expand the focus and participation beyond the southeastern U.S., supporting a transdisciplinary community of researchers, educators, extension experts and industries. Since the last meeting planning, there has been an explosion of new research, tools and challenges around AI in Agriculture across diverse domains. These are quickly evolving areas so communication opportunities to share ideas, best practices, and issues will increase the competitiveness of U.S. agriculture and minimize silos. We expect 400 attendees from across the country as well as over 100 online participants. Local hosts include Texas A&M University (1862 Land-Grant), Prairie View A&M University (1890 Land-Grant) and Texas A&M AgriLife Research (experiment station). Meeting programming is co-organized by Hatch Multi-State project, S1090: AI in Agroecosystems: Big Data and Smart Technology-Driven Sustainable Production. The planning committees, speakers and panelists will come from across domains and geographies. Outputs will include presentations, preprints, and a conference report. Outcomes include new areas of research and approaches for teaching and education, in addition to understanding and collaboration. Impacts will be made through broadening participation and growing communities and their use of AI in agriculture.
Animal Health Component
60%
Research Effort Categories
Basic
20%
Applied
60%
Developmental
20%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
4027410303030%
2057410303010%
3077410303010%
8037410303010%
1027410303010%
6057410303010%
9037410303010%
1327410303010%
Knowledge Area
803 - Sociological and Technological Change Affecting Individuals, Families, and Communities; 102 - Soil, Plant, Water, Nutrient Relationships; 605 - Natural Resource and Environmental Economics; 205 - Plant Management Systems; 132 - Weather and Climate; 903 - Communication, Education, and Information Delivery; 307 - Animal Management Systems; 402 - Engineering Systems and Equipment;

Subject Of Investigation
7410 - General technology;

Field Of Science
3030 - Information and communication;
Goals / Objectives
This project willhost a conference on artificial intelligence (AI) in agriculture April 15-17, 2024 in College Station, Texas. The conference will focus on AI for "Innovation and Discovery to Manage Sustainability in a New World of Environmental Stress".The overall goal of the conference is to strengthen knowledge sharing and collaboration among U.S. university faculty and students,industry, and stakeholders for implementing AI technology into U.S. agriculture and naturalresources an efficient, sustainable, and socio-economically equitable manner.The primary objective is to host a conference that supports this goal.The specific sub-objectives of the conference include:1) Bring agricultural and natural resources researchers, industry, students, representatives from stakeholder groups, and funding agencies together to build collaborations and explore recent developments and opportunities in AI and automation for solving emerging challenges in agricultural production and food systems as well as natural resources.2) Identify AI-driven innovations that are still needed to meet the current and future challenges so U.S. agriculture can remain competitive and profitable while sustaining the environment under achanging climate. Also identifying barriers to implementing these potential innovations.3) Increase knowledge of current agriculture-focused teaching in AI-related initiatives across U.S. universities and strengthen teaching curricula.4) Increase knowledge and skills among graduate students and faculty of the emerging AI techniques available for research and extension.5) Develop transdisciplinary teams for AI projects addressing needs and opportunities in agriculture.6) Develop action plans to overcome existing barriers to developing AI-driven solutions andadopting AI technology in U.S. farm and ranch management systems.7) Identify skills, among farmers, consultants, and extension agents, necessary to manage the farm of the future.8) Strength collaboration among U.S. universities, industry, and stakeholder groups to develop the next generation of AI-driven technologies that will support U.S. agriculture.9) Assemble resources and information relevant to funding agencies, university administrators, members of National Academies of Sciences, Engineering, and Medicine (NAEM), and industry on needs to address current and future challenges of U.S. agriculture. This includes personnel and facilities infrastructure needed to advance innovation.10) Provide a forum where researchers, teachers, extension experts and industry can publicize their work and findings in AI relevant to agriculture and natural resources.
Project Methods
The major effort will be to host the conference, including soliciting abstracts and presenters, organizing location and logistics. The evaluation will include the number and satisfaction of attendees, the number and quality of presentations, as well as the breadth of attendees and what they felt they gained from the conference.

Progress 04/15/24 to 04/14/25

Outputs
Target Audience:Target audience included engineers, data scientists, agricultural economists, plant breeders, agronomists and social scientists. Other faculty and students working on crop science, rural sociology, entomology and plant pathology, food science, horticulture, climatology, natural resources and conservation, computer science, among others were targeted. Extension agents, growers, and other members of commodity stakeholder groups were invited and attended the conference, increasing the reach of the conference. Faculty and students at HBCU (e.g. PVAMU) and other minority serving institutions in agriculture were invited and attended. Wealso targeted a number of companies involved in agriculture, natural resources and its intersection with AI, as well as data science companies interested in agriculture to serve as sponsors, presenters, panelists and attendees. Leaders of USDA/NSF AI in Agriculture projects were contacted to support the conference. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The conference provided an enriching experience, a platform to explore cutting-edge developments in AI and its applications in agriculture. It is our goal that the insightful presentations, stimulating panel discussions, and networking opportunities contributed to sharing of knowledge and growth of the scientific mission. Many of the attendees were early career professors, postdoctoral scholars, graduate students and undergraduates. How have the results been disseminated to communities of interest?Every session was broadcast live via YouTube Live, and the recordings can still be found on YouTube.https://drive.google.com/file/d/14rq8sCZUU0QEHkHUgGoxBpoC_qmAgV3r/view The links to those have been shared with attendees but are public so that anyone can view them. Copies of the poster presentations were posted on the conference website.https://agriliferegister.tamu.edu/website/63088/ What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? The 2024 Artificial Intelligence in Agriculture conference took place from April 15 to 17, 2024, at the Texas A&M Hotel and Conference Center in College Station, Texas, and welcomed 326 attendees, 59 poster presentations, and 110 speakers. This year marks the thirdAI in Agriculture conference, which contributed to continuing the mission of enhancing knowledge sharing and fostering collaboration among university faculty, students, industry professionals, and stakeholders. The aim was to facilitate the efficient, sustainable, and socio-economically equitable implementation of artificial intelligence (AI) technology in the agricultural sector. The conference included a combination of invited plenary presentations, four panel sessions, twenty concurrent breakout sessions that include oral presentations (combining invited and volunteer abstracts submitted by participants) and facilitated working sessions, and a poster session (abstracts submitted by conference participants) with awards. Four pre-conference workshops were held to provide attendees with hands-on applications in data processing and modeling. Plenary speakers from Microsoft, IBM, and the University of Nebraska-Lincoln statistics department shared advancements that further the mission of the conference. Expert panels included producers, representatives from USDA/NSF AI Institutes, experts in water system innovations, and industry and technology leaders.

Publications