• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Big data and machine learning for crop protection

    Author(s)
    Ip, RHL
    Ang, LM
    Seng, KP
    Broster, JC
    Pratley, JE
    Griffith University Author(s)
    Ang, Li-minn (Kenneth)
    Year published
    2018
    Metadata
    Show full item record
    Abstract
    Crop protection is the science and practice of managing plant diseases, weeds and other pests. Weed management and control are important given that crop yield losses caused by pests and weeds are high. However, farmers face increased complexity of weed control due to evolved resistance to herbicides. This paper first presents a brief review of some significant research efforts in crop protection using Big data with the focus on weed control and management followed by some potential applications. Some machine learning techniques for Big data analytics are also reviewed. The outlook for Big data and machine learning in crop ...
    View more >
    Crop protection is the science and practice of managing plant diseases, weeds and other pests. Weed management and control are important given that crop yield losses caused by pests and weeds are high. However, farmers face increased complexity of weed control due to evolved resistance to herbicides. This paper first presents a brief review of some significant research efforts in crop protection using Big data with the focus on weed control and management followed by some potential applications. Some machine learning techniques for Big data analytics are also reviewed. The outlook for Big data and machine learning in crop protection is very promising. The potential of using Markov random fields (MRF) which takes into account the spatial component among neighboring sites for herbicide resistance modeling of ryegrass is then explored. To the best of our knowledge, no similar work of modeling herbicide resistance using the MRF has been reported. Experiments and data analytics have been performed on data collected from farms in Australia. Results have revealed the good performance of our approach.
    View less >
    Journal Title
    Computers and Electronics in Agriculture
    Volume
    151
    DOI
    https://doi.org/10.1016/j.compag.2018.06.008
    Subject
    Agricultural, veterinary and food sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/384180
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander