Advances in agricultural remote sensing and artificial intelligence 2
The session recording will be archived on the site until June 25th, 2023
This session was streamed but not recorded
Date: 3/27/2023
Time: 10:20 AM - 11:40 AM
Room: Virtual 8
Type: Virtual Paper,
Theme:
Curated Track:
Sponsor Group(s):
Geographic Information Science and Systems Specialty Group, Remote Sensing Specialty Group
Organizer(s):
Zijun Yang University of Illinois at Urbana-Champaign
Chunyuan Diao University of Illinois at Urbana-Champaign
Chair(s):
Zijun Yang University of Illinois Urbana-Champaign
Chunyuan Diao University of Illinois Urbana-Champaign
Description:
Climate change and increased climate variability have posed great challenges to food security given the continuous increasing global population. More comprehensive understandings of agricultural system dynamics and their responses to climatic and environmental changes are urgently needed. With the recent advances in remote sensing technologies, new satellite missions and remote sensing datasets keep emerging. The developments of artificial intelligence (AI) and machine learning, along with geospatial big data of various spatial, temporal, and spectral resolutions, have unprecedentedly enabled us to better understand the dynamics of natural and human-induced processes in agricultural systems. The improved modeling capabilities of the advanced AI models facilitate the assessments of impacts of climate change on agriculture (e.g., changes in land use dynamics, crop phenology, crop yield, etc.) using variables derived from remote sensing and other sources of data. This session calls for submissions discussing the advances in theory, methodology, and/or applications in agricultural systems using geospatial big data with AI methods. Potential topics may include but not be limited to:
- Climate and agricultural system monitoring
- Data generation with remote sensing and AI for agricultural systems
- Modeling and quantification of crop responses to climate change
- Remote identification of crop type, crop phenology, and/or crop growth condition
- Field-level applications for precision agriculture
- Near-real-time applications in agricultural remote sensing
- AI for smart farming
- Cyberinfrastructure for agricultural remote sensing and AI
Presentations (if applicable) and Session Agenda:
Chunyuan Diao, University of Illinois at Urbana-Champaign |
Development of large-scale crop phenological characterization framework with satellite time series |
Bo Shan |
Deriving Canopy Nutrient Content using Hyperspectral Data and Machine Learning for Cannabis Sativa |
Min Xu |
Estimating estuarine primary production using satellite data and machine learning |
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Advances in agricultural remote sensing and artificial intelligence 2
Description
Type: Virtual Paper,
Date: 3/27/2023
Time: 10:20 AM - 11:40 AM
Room: Virtual 8
Contact the Primary Organizer
Zijun Yang University of Illinois at Urbana-Champaign
zijuny2@illinois.edu
Session Sponsored By
