Decentralized Intelligence for Smart Agriculture

Pascal Faye, Jeanne Faye, Mariane Senghor

2024

Abstract

This work proposes a model called AIMS (Agricultural Information and Management System) based on some Machine Learning Algorithm (ML) as CART (Classification And Regression Trees), KNN(K-nearest neighbors) and SVM(Support Vector Machine). It describes both a multi-agent system and Internet Of Things device that ensures data collection and control as well as a data monitoring system via our web platform for decision-making support in a real-world agricultural environments. This for a prompt, effective and sustainable agricultural development. We refer to cases in which agent collaboration is needed for efficient task execution (e. g. data processing and decision making). In our context, dynamics and uncertainty prohibit computation strategies ahead of task execution. Combining methods from Machine Learning (ML), Markov decision processes (MDP) and probability, we introduce an auto-stabilizing coordination mechanism.

Paper Citation

in Harvard Style

Faye P., Faye J. and Senghor M. (2024). Decentralized Intelligence for Smart Agriculture. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 240-247. DOI: 10.5220/0012342100003636

in Bibtex Style

@conference{icaart24,
author={Pascal Faye and Jeanne Faye and Mariane Senghor},
title={Decentralized Intelligence for Smart Agriculture},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2024},
pages={240-247},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012342100003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Decentralized Intelligence for Smart Agriculture
SN - 978-989-758-680-4
AU - Faye P.
AU - Faye J.
AU - Senghor M.
PY - 2024
SP - 240
EP - 247
DO - 10.5220/0012342100003636
PB - SciTePress