  This work addresses task planning under uncertainty for precision agriculture
applications whereby task costs are uncertain and the gain of completing a task
is proportional to resource consumption (such as water consumption in precision
irrigation). The goal is to complete all tasks while prioritizing those that
are more urgent, and subject to diverse budget thresholds and stochastic costs
for tasks. To describe agriculture-related environments that incorporate
stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph
(SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action
Planning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabled
by SAG, and tackles the task planning problem by simultaneously determining the
optimal tasks to perform and an optimal time to exit (i.e. return to a base
station), at run-time. The proposed approach is tested with both simulated data
and real-world experimental datasets collected in a commercial vineyard, in
both single- and multi-robot scenarios. In all cases, NBA-P outperforms other
evaluated methods in terms of return per visited vertex, wasted resources
resulting from aborted tasks (i.e. when a budget threshold is exceeded), and
total visited vertices.
