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To meet the 2050 global crop demand, production must increase by 25% to 75% (Hunter et al., 2017). Amid challenges such as climate change, water scarcity and pests, growers need to consider emerging technologies to boost productivity. Traditionally, many agricultural practices are based on empirical knowledge and cannot account for the complex interactions between the numerous factors that affect crop growth. The limits of empirical knowledge are reached when farmers need to make decisions at the within-field level to address variability in production, which is in essence precision agriculture (PA). Integrating big data and machine learning (ML) in agriculture offers the opportunity to unravel the factors and represent the interactions causing this variation, forming the basis for data-driven decision-support tools.

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The role of big data and machine learning methods in precision agriculture

  • 章节

  • 发表日期：

    2025年

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  • Dhahi Al-Shammari,
  • Si Yang Han,
  • Patrick Filippi,
  • Nikolas Hoskin,
  • Sally Poole,
  • Niranjan S. Wimalathunge,
  • Jie Wang,
  • Thomas F. A. Bishop

摘要

To meet the 2050 global crop demand, production must increase by 25% to 75% (Hunter et al., 2017). Amid challenges such as climate change, water scarcity and pests, growers need to consider emerging technologies to boost productivity. Traditionally, many agricultural practices are based on empirical knowledge and cannot account for the complex interactions between the numerous factors that affect crop growth. The limits of empirical knowledge are reached when farmers need to make decisions at the within-field level to address variability in production, which is in essence precision agriculture (PA). Integrating big data and machine learning (ML) in agriculture offers the opportunity to unravel the factors and represent the interactions causing this variation, forming the basis for data-driven decision-support tools.