  Monitoring the health and vigor of grasslands is vital for informing
management decisions to optimize rotational grazing in agriculture
applications. To take advantage of forage resources and improve land
productivity, we require knowledge of pastureland growth patterns that is
simply unavailable at state of the art. In this paper, we propose to deploy a
team of robots to monitor the evolution of an unknown pastureland environment
to fulfill the above goal. To monitor such an environment, which usually
evolves slowly, we need to design a strategy for rapid assessment of the
environment over large areas at a low cost. Thus, we propose an integrated
pipeline comprising of data synthesis, deep neural network training and
prediction along with a multi-robot deployment algorithm that monitors
pasturelands intermittently. Specifically, using expert-informed agricultural
data coupled with novel data synthesis in ROS Gazebo, we first propose a new
neural network architecture to learn the spatiotemporal dynamics of the
environment. Such predictions help us to understand pastureland growth patterns
on large scales and make appropriate monitoring decisions for the future. Based
on our predictions, we then design an intermittent multi-robot deployment
policy for low-cost monitoring. Finally, we compare the proposed pipeline with
other methods, from data synthesis to prediction and planning, to corroborate
our pipeline's performance.
