Big data and machine learning for crop protection

R H L Ip; Li-Minn Ang; K P Seng; J C Broster; J E Pratley

doi:10.1016/j.compag.2018.06.008

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Big data and machine learning for crop protection

Journal article

Peer reviewed

Big data and machine learning for crop protection

R H L Ip, Li-Minn Ang, K P Seng, J C Broster and J E Pratley

Computers and Electronics in Agriculture, Vol.151, pp.376-383

2018

DOI: https://doi.org/10.1016/j.compag.2018.06.008

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Abstract

big data

machine learning

crop protection

weed control

herbicide resistance

Markov random field model

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.

Details

Title: Big data and machine learning for crop protection
Authors: R H L Ip (Author) - Charles Sturt University
Li-Minn Ang (Author) - Charles Sturt University
K P Seng (Author) - Charles Sturt University
J C Broster (Author) - Charles Sturt University
J E Pratley (Author) - Charles Sturt University
Publication details: Computers and Electronics in Agriculture, Vol.151, pp.376-383
Publisher: Elsevier BV
DOI: 10.1016/j.compag.2018.06.008
ISSN: 0168-1699
Organisation Unit: Engage Research Lab; School of Science, Technology and Engineering; School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland
Language: English
Record Identifier: 99450974602621
Output Type: Journal article

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62 Times Cited - Web of Science

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Collaboration types: Domestic collaboration
Web Of Science research areas: Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications

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Source: InCites