  We propose a generalized decision-theoretic system for a heterogeneous team
of autonomous agents who are tasked with online identification of
phenotypically expressed stress in crop fields.. This system employs four
distinct types of agents, specific to four available sensor modalities:
satellites (Layer 3), uninhabited aerial vehicles (L2), uninhabited ground
vehicles (L1), and static ground-level sensors (L0). Layers 3, 2, and 1 are
tasked with performing image processing at the available resolution of the
sensor modality and, along with data generated by layer 0 sensors, identify
erroneous differences that arise over time. Our goal is to limit the use of the
more computationally and temporally expensive subsequent layers. Therefore,
from layer 3 to 1, each layer only investigates areas that previous layers have
identified as potentially afflicted by stress. We introduce a reinforcement
learning technique based on Perkins' Monte Carlo Exploring Starts for a
generalized Markovian model for each layer's decision problem, and label the
system the Agricultural Distributed Decision Framework (ADDF). As our domain is
real-world and online, we illustrate implementations of the two major
components of our system: a clustering-based image processing methodology and a
two-layer POMDP implementation.
