English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Towards a Decentralized, Autonomous Multiagent Framework for Mitigating Crop Loss