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Internet of Things in arable farming: Implementation, applications, challenges and potential

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  • Andrés Villa-Henriksen, Agro Intelligence Aps
  • ,
  • Gareth T.C. Edwards, Agro Intelligence Aps
  • ,
  • Liisa A. Pesonen, Luke Natural Resources Institute Finland
  • ,
  • Ole Green, Agro Intelligence Aps
  • ,
  • Claus Aage Grøn Sørensen

The Internet of Things is allowing agriculture, here specifically arable farming, to become data-driven, leading to more timely and cost-effective production and management of farms, and at the same time reducing their environmental impact. This review is addressing an analytical survey of the current and potential application of Internet of Things in arable farming, where spatial data, highly varying environments, task diversity and mobile devices pose unique challenges to be overcome compared to other agricultural systems. The review contributes an overview of the state of the art of technologies deployed. It provides an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. Lastly, it presents some future directions for the Internet of Things in arable farming. Current issues such as smart phones, intelligent management of Wireless Sensor Networks, middleware platforms, integrated Farm Management Information Systems across the supply chain, or autonomous vehicles and robotics stand out because of their potential to lead arable farming to smart arable farming. During the implementation, different challenges are encountered, and here interoperability is a key major hurdle throughout all the layers in the architecture of an Internet of Things system, which can be addressed by shared standards and protocols. Challenges such as affordability, device power consumption, network latency, Big Data analysis, data privacy and security, among others, have been identified by the articles reviewed and are discussed in detail. Different solutions to all identified challenges are presented addressing technologies such as machine learning, middleware platforms, or intelligent data management.

Original languageEnglish
JournalBiosystems Engineering
Pages (from-to)60-84
Number of pages25
Publication statusPublished - Mar 2020

    Research areas

  • Big data, Farm management information system, Internet of things, Machine learning, Smart farming, Wireless sensor network

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