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<p>The purpose of this study develops and analyzes an integrated framework that combines Big Data analytics and Machine Learning (ML) techniques to enhance agricultural productivity within the smart village ecosystem. In addition to conducting a bibliometric and systematic review of peer-reviewed literature, the research proposes a conceptual model illustrating how data-driven technologies can strengthen rural decision support systems. The proposed framework is validated conceptually through synthesis of prior empirical findings and structured analysis using Scopus-indexed publications from 2019 to 2024. Results from the reviewed literature indicate that the integration of Big Data and ML has been associated with improved prediction accuracy, early pest detection, irrigation optimization, and enhanced decision-support capabilities in agricultural systems. These outcomes reflect synthesized findings from prior empirical studies rather than original modeling conducted in this research. However, the framework proposed in this study is conceptually validated through systematic literature synthesis rather than direct field experimentation. Therefore, while findings highlight promising implications, empirical validation in real-world smart village environments remains necessary. The study acknowledges limitations related to secondary data reliance, infrastructure disparities, and digital readiness gaps among rural communities.</p>

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Integrating Big Data and Machine Learning to support smart village decisions for agricultural productivity improvement

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月14日

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  • Sukriadi,
  • Andi Adawiah,
  • Andi Muhammad Nurul Afdhal

摘要

The purpose of this study develops and analyzes an integrated framework that combines Big Data analytics and Machine Learning (ML) techniques to enhance agricultural productivity within the smart village ecosystem. In addition to conducting a bibliometric and systematic review of peer-reviewed literature, the research proposes a conceptual model illustrating how data-driven technologies can strengthen rural decision support systems. The proposed framework is validated conceptually through synthesis of prior empirical findings and structured analysis using Scopus-indexed publications from 2019 to 2024. Results from the reviewed literature indicate that the integration of Big Data and ML has been associated with improved prediction accuracy, early pest detection, irrigation optimization, and enhanced decision-support capabilities in agricultural systems. These outcomes reflect synthesized findings from prior empirical studies rather than original modeling conducted in this research. However, the framework proposed in this study is conceptually validated through systematic literature synthesis rather than direct field experimentation. Therefore, while findings highlight promising implications, empirical validation in real-world smart village environments remains necessary. The study acknowledges limitations related to secondary data reliance, infrastructure disparities, and digital readiness gaps among rural communities.