Session 3 - Precision Agriculture: Multi-Resolution Approximations for Precision Agriculture
Presentation Type
Oral
Student
Yes
Track
Precision Ag/Biological Sciences Application
Abstract
Precision agriculture is the leveraging of data for better farming practices. An important aspect of precision agriculture is analyzing the effect of covariates on crop yield. Agricultural data sets can be very large, making likelihood-based inference on traditional spatial models computationally burdensome. The Multi-Resolution Approximation allows for fast inference on Gaussian processes by using a particular covariance structure. We show through a simulation study and the analysis of a real agricultural data set that the Multi-Resolution Approximation can be used to estimate covariate effects with near-identical accuracy as traditional likelihood estimation, and with great computational advantage.
Start Date
2-11-2020 9:30 AM
End Date
2-11-2020 10:30 AM
Session 3 - Precision Agriculture: Multi-Resolution Approximations for Precision Agriculture
Dakota Room 250 A/C
Precision agriculture is the leveraging of data for better farming practices. An important aspect of precision agriculture is analyzing the effect of covariates on crop yield. Agricultural data sets can be very large, making likelihood-based inference on traditional spatial models computationally burdensome. The Multi-Resolution Approximation allows for fast inference on Gaussian processes by using a particular covariance structure. We show through a simulation study and the analysis of a real agricultural data set that the Multi-Resolution Approximation can be used to estimate covariate effects with near-identical accuracy as traditional likelihood estimation, and with great computational advantage.