B049
GeoAI-driven Sustainable Agroecosystems: Current state, Applications, & Future Directions
Session ID#: 251138
Session Description:
We are on the verge of a technological revolution in the field of agroecosystems. The fusion of GIS, remote sensing, and AI has created a remarkable paradigm shift, ushering in a new era of geospatial AI (GeoAI). As the challenges facing the agricultural sector intensify, the synergy of GeoAI, accentuated by the recent surge in cloud computing, robotics, and foundational model research. This session will focus on the development and application of GeoAI to address challenges within the agroecosystem, such as AI-driven analytics in digital agriculture; solutions for optimizing management; predictive modeling of extreme weather effects, developing of more adaptive agricultural practices (e.g., cover crops, tillage); Drone/Robotics-enabled plant phenotyping and breeding; and addressing data availability, privacy, security, and ethical implications of AI. Research on developing models and analytics, and working with end users (e.g., farmers, agronomists, biologists) to implement novel approaches to increase efficiency and improve sustainability will be welcome.
Co-Sponsor(s):
Index Terms:
0402 Agricultural systems [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
1928 GIS science [INFORMATICS]
1942 Machine learning [INFORMATICS]
Primary Convener: Yuchi Ma, Stanford University, Department of Earth System Science & Center on Food Security and the Environment, Stanford, CA, United States
Conveners: Ignacio Ciampitti, Purdue University, Department of Agronomy, West Lafayette, United States, Phuong Dao, Department of Agricultural Biology, Colorado State University, 307 University Avenue, Fort Collins, CO 80523, USA, Fort Collins, United States and Jing Zhou, Oregon State University, CROP AND SOIL SCIENCE, Corvallis, United States
Student/Early Career Convener: Shuo Chen, Purdue University, Department of Earth, Atmospheric, and Planetary Sciences, West Lafayette, United States
Safeguarding Traditions, Enhancing Agriculture: Indigenous Data Sovereignty as a Tool in the Age of AI Centered Agriculture (1857505)
Lydia Jennings1, Breanna Lameman2, Andrew Martinez3, Jewel Cummins2, Stephanie Carroll4 and Breanna Lameman, (1)Dartmouth College, Hanover, United States, (2)University of Arizona, Tucson, United States, (3)University of Arizona, Tucson, AZ, United States, (4)University of Arizona, College of Public Health, Tucson, United States
Artificial Intelligence in Sustainable Potato Production: Nitrogen Management and Yield Prediction through Spectral Remote Sensing (1862037)
Alfadhl Alkhaled, University of Maryland Eastern Shore, Princess Anne, MD, United States, Philip A Townsend, University of Wisconsin Madison, Department of Forest and Wildlife Ecology, Madison, United States and YI WANG, University of Wisconsin - Madison, Horticulture, Madison, United States
Generalizability of Remote Sensing Based Data-Driven Plot-Scale in-Season Crop Yield Forecasting Models (1879019)
Karisma Yumnam1, Yufeng Ge2, Omar Murad2, Troy Gilmore3, Frank Bai4 and Jennifer Keshwani5, (1)University of Nebraska Lincoln, Department of Biological Systems Engineering, Lincoln, NE, United States, (2)University of Nebraska Lincoln, Department of Biological Systems Engineering, Lincoln, United States, (3)University of Nebraska-Lincoln, Conservation and Survey Division, School of Natural Resources, Lincoln, United States, (4)North Carolina State University Raleigh, Department of Biological and Agricultural Engineering, Raleigh, United States, (5)University of Nebraska, Lincoln, NE, United States
Assessing Corn Nitrogen Status at Key Growth Stages Through Multimodal Approaches with Ultra-High Temporal, Spectral, and Spatial Resolution (1904757)
Fengkai Tian1, Guojie Ruan2 and Jianfeng Zhou2, (1)University of Missouri, Department of Chemical and Biomedical Engineering, Columbia, United States, (2)University of Missouri, Division of Plant Science and Technology, Columbia, United States
Toward High Spatial Resolution Digital Soil Health Mapping Using Sensing-based Data: A Case Study in Soybean Experimental Plots (1932789)
Guojie Ruan1, Fengkai Tian2, Blessing Ademola2, Jianfeng Zhou1, Noel R Aloysius2 and Timothy M Reinbott3, (1)University of Missouri, Division of Plant Science and Technology, Columbia, United States, (2)University of Missouri, Department of Chemical and Biomedical Engineering, Columbia, United States, (3)University of Missouri, MO-AES Field Operations, Columbia, MO, United States
Digital Twins and GeoAI: Revolutionizing the Future of Sustainable Agriculture (Invited) (1947265)
Kaiyu Guan1, Bin Peng2, Sheng Wang1, Xinlei Wang1, Zhao Jiang1, Jiawei Fan1, Licheng Liu3, Zhenong Jin4, Jinyun Tang5, Rongzhu Qin1 and Kejie Zhao1, (1)University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, (2)University of Illinois Urbana-Champaign, Urbana, United States, (3)University of Minnesota Twin Cities, Department of Bioproducts and Biosystems Engineering, St. Paul, United States, (4)University of Minnesota Twin Cities, Department of Bioproducts and Biosystems Engineering, Saint Paul, United States, (5)Lawrence Berkeley Natl Lab, Climate and Ecosystem Sciences Division, Berkeley, United States
Hyperspectral Imaging and Machine Learning Models for Detection of Grapevine Red Blotch Virus in Vineyards (1963017)
Eve Laroche, California State University Fresno, Viticulture and Enology, Fresno, CA, United States, Monica Cooper, University of California Agriculture and Natural Resources, Napa, United States, Marc Fuchs, Cornell University, School of Integrative Plant Science, Ithaca, United States and Luca Brillante, California State University Fresno, Viticulture and Enology, Fresno, United States
A secure, privacy-preserving Microsoft Confidential Clean Room to bridge the data-sharing gap between farmers and agri-tech innovators (1971998)
Guanyuan Shuai1, Alyssa K Whitcraft2, Ritvik Sahajpal2, Deepak Joshi3, Anand Shankar4 and Harikrishna Prabhu3, (1)University of Maryland College Park, Geographical Sciences, College park, United States, (2)University of Maryland College Park, Geographical Sciences, College Park, United States, (3)Microsoft, Hyderabad, India, (4)Microsoft, Seattle, United States
