Big Data for weed control and crop protection

F.K. van Evert*, S. Fountas, D. Jakovetic, V. Crnojevic, I. Travlos, C. Kempenaar

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

50 Citations (Scopus)


Farmers have access to many data-intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide-resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established.
Original languageEnglish
Pages (from-to)218-233
JournalWeed Research
Issue number4
Publication statusPublished - 2017


  • Data ownership
  • Data sharing
  • Graphical model
  • Multivariate regression
  • Neural network
  • Semantics
  • Support vector machine


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