Multivariate Active Learning and Adaptive Sampling With Multi-Kernel Gaussian Processes

In agriculture, understanding the distribution and relationship between different aspects of the environment is important for minimizing chemical use and reducing environmental impact. Traditionally, it is done by manually collecting samples on the field and then sending them to a laboratory for analysis. This is not only labor-intensive and costly, but the results will still be outdated. There is thus a growing interest in developing robotic systems to map these variables and uncover their correlations in real time. However, existing learning and sampling methods only focus on one quantity of interest (QoI) or make assumptions that might lead to sub-optimal results when there are multiple QoIs. In this work, we propose a multivariate active transfer learning and intelligent adaptive sampling system that can simultaneously learn the most accurate models for multiple QoIsas well as the relationship between them, and leverage that knowledge to select the next best locations to sample. Performance benchmarking against existing methods shows that QoIsare mapped more accurately, complex correlations between QoIsare identified more precisely, and travel routes are planned more efficiently.