The shortage of qualified human labor is a key challenge facing farmers, limiting profit
margins and preventing the adoption of sustainable and diversified agroecosystems, such …

In order to deploy robots in previously unseen and unstructured environments, the robots
should have the capacity to learn on their own and adapt to the changes in the …

This work presents advances in predictive modeling of weed growth, as well as an improved
planning index to be used in conjunction with these techniques, for the purpose of improving …

We present a method to estimate two-dimensional, time-invariant oceanic flow fields based
on data from both ensemble forecasts and online measurements. Our method produces a …

This work presents a strategy for coordinated multi-agent weeding under conditions of
partial environmental information. The goal of this work is to demonstrate the feasibility of …

Localization measurements for an autonomous underwater vehicle (AUV) are often difficult
to obtain. In many cases, localization measurements are only available sporadically after the …

Monitoring and modeling large-scale stochastic phenomena with both spatial and temporal
(spatiotemporal) evolution by using a network of distributed sensors is a critical problem in …

We present a planning framework for min-imising the deterministic worst-case error in
sparse Gaus-sian process (GP) regression. We first derive a univer-sal worst-case error …

We present a differentially-constrained machine learning model that can generalize over
similar spatiotemporally evolving dynamical systems. It is shown that not only can an E-GP …

A wide variety of real life complex networks are prohibitively large for modeling, analysis and
control. Understanding the structure and dynamics of such networks entails creating a …