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The challenges of water loss and inadequate scheduling in traditional irrigation methods hinder the ability to meet the increasing demand for food production while also conserving valuable water resources. In order to overcome these issues, this study introduces an advanced smart irrigation system that harnesses the capabilities of the Internet of Things (IoT), data analysis, and machine learning. The primary objective is to determine the most effective method for applying and scheduling irrigation, thus optimizing resource usage. This innovative system relies on a meticulously curated dataset, “INTELLIGENT IRRIGATION SYSTEM” obtained from Kaggle. The dataset encompasses four essential attributes, extensively detailed in the metadata, providing foundational information crucial for precise irrigation management. This paper proposes an intelligent irrigation approach that can be deployed in different contexts. This approach is based on machine learning algorithms. Multiple models, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and Gaussian process (GP) classifier, were employed in the development of this approach. The results showed that LR and GP are better with an accuracy of 0.9833, precision of 0.9782, F1 score of 0.9890, specificity of 0.9333, and sensitivity of 1.0 compared to KNN model.

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Applying Machine Learning Approaches with Integrated Internet of Things for Water Management System

  • 会议论文

  • 发表日期：

    2025年

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  • Meroua Belmir,
  • Wafa Difallah,
  • Abdelkader Ghazli

摘要

The challenges of water loss and inadequate scheduling in traditional irrigation methods hinder the ability to meet the increasing demand for food production while also conserving valuable water resources. In order to overcome these issues, this study introduces an advanced smart irrigation system that harnesses the capabilities of the Internet of Things (IoT), data analysis, and machine learning. The primary objective is to determine the most effective method for applying and scheduling irrigation, thus optimizing resource usage. This innovative system relies on a meticulously curated dataset, “INTELLIGENT IRRIGATION SYSTEM” obtained from Kaggle. The dataset encompasses four essential attributes, extensively detailed in the metadata, providing foundational information crucial for precise irrigation management. This paper proposes an intelligent irrigation approach that can be deployed in different contexts. This approach is based on machine learning algorithms. Multiple models, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and Gaussian process (GP) classifier, were employed in the development of this approach. The results showed that LR and GP are better with an accuracy of 0.9833, precision of 0.9782, F1 score of 0.9890, specificity of 0.9333, and sensitivity of 1.0 compared to KNN model.