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Modern agriculture faces the dual challenge of increasing crop yields to meet growing food demand while mitigating environmental impacts such as water scarcity and climate change. This paper proposes a novel approach to address these challenges by integrating machine learning (ML) algorithms and Internet of Things (IoT) technologies into precision irrigation and weather forecasting practices. By harnessing the power of data-driven insights, this research aims to revolutionize agricultural management, offering farmers unprecedented precision in water resource allocation and proactive measures against weather-related risks. Through a comprehensive review of existing literature, detailed methodology, and empirical case studies, the paper demonstrates the potential of ML and IoT to reshape crop yield enhancement strategies. The findings underscore the transformative impact of these technologies on resource efficiency, economic viability, and environmental sustainability in modern agriculture, paving the way for a more resilient and productive farming future.

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Farming in the Digital Era: Optimizing Crop Yield with Machine Learning and IoT-Driven Weather Forecasting and Precision Irrigation

  • 会议论文

  • 发表日期：

    2025年

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  • Shreeya Jog,
  • Jui Bhole,
  • Aditi Gaikwad,
  • Aryaman Modi,
  • Chaya Jadhav,
  • Pooja Sakunde

摘要

Modern agriculture faces the dual challenge of increasing crop yields to meet growing food demand while mitigating environmental impacts such as water scarcity and climate change. This paper proposes a novel approach to address these challenges by integrating machine learning (ML) algorithms and Internet of Things (IoT) technologies into precision irrigation and weather forecasting practices. By harnessing the power of data-driven insights, this research aims to revolutionize agricultural management, offering farmers unprecedented precision in water resource allocation and proactive measures against weather-related risks. Through a comprehensive review of existing literature, detailed methodology, and empirical case studies, the paper demonstrates the potential of ML and IoT to reshape crop yield enhancement strategies. The findings underscore the transformative impact of these technologies on resource efficiency, economic viability, and environmental sustainability in modern agriculture, paving the way for a more resilient and productive farming future.