Embedded Bi-IoT Irrigation System Driven by Artificial Intelligence for Optimized Agricultural Water Management

EasyChair Preprint 15667

Abstract

Efficient management of water resources in agriculture is a major challenge, particularly in the face of climate change and increasing food demand. Traditional irrigation systems, often static and based on predetermined schedules, result in water wastage and reduced yields. This paper proposes a conceptual modeling approach for an embedded Bi-IoT irrigation system driven by Artificial Intelligence (AI), aiming to optimize water usage and improve agricultural productivity. We introduce a formal framework in which the system state is defined by a vector of environmental characteristics, the action corresponds to the quantity of water delivered, and the yield is modeled by a complex function (e.g., a neural network) trained on historical data. Although this work is still at a preliminary stage without finalized numerical results, it provides a solid theoretical basis for the future design of optimal and dynamic irrigation policies, leveraging IoT and AI technologies as well as reinforcement learning methods.

Keyphrases: Artificial Intelligence, IoT, Irrigation, Optimization, Optimized Agricultural Water Management, Reinforcement Learning, sustainable agriculture

@booklet{EasyChair:15667,
  author    = {Youssef Balouki and Imane Lmati and Youssef Zarouali},
  title     = {Embedded Bi-IoT Irrigation System Driven by Artificial Intelligence for Optimized Agricultural Water Management},
  howpublished = {EasyChair Preprint 15667},
  year      = {EasyChair, 2025}}