A Phenology-guided Bayesian-CNN (PB-CNN) Framework for Yield Estimation and Uncertainty Analysis
Chishan Zhang presented at the SERVIR's Geo-AI Working Group on June 26, 2024.
Chishan Zhang is a fifth-year Ph.D. candidate in the Department of Geography & Geographic Information Science at University of Illinois Urbana-Champaign. Specializing in the application of remote sensing technologies, Chishan's research integrates advanced machine learning techniques with satellite data to enhance the generalizability and transferability of agricultural yield estimation.
In this talk, Chishan will present the Phenology-guided Bayesian Convolutional Neural Network (PB-CNN) framework, a novel approach for improving county-level crop yield estimation. This framework incorporates critical crop growth mechanisms and Bayesian theory into the modeling process. By focusing on the distinct roles of various environmental conditions throughout the crop phenological stages, the PB-CNN framework not only improves yield estimation accuracy but also deepens our understanding of the uncertainties inherent in the yield prediction process.
Find Chishan presentation here:
Find relevant publication here: https://drive.google.com/file/d/1RZnO...
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