FR3.P3.5

Unsupervised Adversarial Domain Adaptation to Predict Maize Grain Yield Across Environments

Claudia Aviles Toledo, Melba Crawford, Purdue University, United States
Session:
FR3.P3: Multisource Remote Sensing for Enhanced Monitoring and Assessing Agricultural Land Applications III Oral
Track:
Community-Contributed Sessions
Location:
Plaza: Room P3
Presentation Time:
Friday, 8 August, 14:15 - 14:30
Session Co-Chairs:
Jonathan Richetti, Commonwealth Scientific and Industrial Research Organization (CSIRO) and Nasem Badreldin, University of Manitoba
Presentation
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Session FR3.P3
FR3.P3.1: A COMPARATIVE STUDY OF MULTISPECTRAL CAMERAS AND ACTIVE INSTRUMENTS FOR PREDICTING PASTURE BIOMASS
Elizabeth Morse-McNabb, MD Farhad Hasan, Anna Thomson, Anna Weeks, Agriculture Victoria Research, Australia
FR3.P3.2: ESTIMATING PADDY YIELD IN SMALLHOLDER FARM SETTINGS USING A SPATIAL HIERARCHICAL APPROACH
Uma Shankar Panday, Kathmandu University, Nepal; Arun Kumar Pratihast, Wageningen University and Research, Netherlands; Jagannath Aryal, The University of Melbourne, Australia; Rijan Bhakta Kayastha, Kathmandu University, Nepal
FR3.P3.3: LARGE-SCALE DETECTION OF VERTICILLIUM WILT IN COTTON USING SENTINEL-2 IMAGERY
Abhasha Joshi, Thomas Bishop, Patrick Filippi, The University of Sydney, Australia
FR3.P3.4: Leveraging Sentinel-2 for Field-Scale Nitrogen Estimation in Australian Rice Fields
Sunil Kumar Jha, James Brinkhoff, Andrew J. Robson, University of New England, Australia; Brian W. Dunn, Department of Primary Industries and Regional Development, Australia
FR3.P3.5: Unsupervised Adversarial Domain Adaptation to Predict Maize Grain Yield Across Environments
Claudia Aviles Toledo, Melba Crawford, Purdue University, United States
Resources
No resources available.