Towards automating visual in-field monitoring of crop health

David Gibson, Tilo Burghardt, Neill Campbell, Nishan Canagarajah

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)
136 Downloads (Pure)


We present an application that demonstrates a proof of concept system for automated in-the-field monitoring of disease in wheat crops. Such in-situ applications are required to be robust in the presence of clutter, provide rapid and accurate analysis and are able to operate at scale. We propose a processing pipeline that detects key wheat diseases in cluttered field imagery. First, we describe and evaluate a high dimensional texture descriptor combined with a randomised forest approach for automated primary leaf recognition. Second, we show that a combined nearest neighbour classifier and voting system applied to segmented leaf regions can robustly determine the presence and type of disease. The system has been tested on a real-world database of images of wheat leaves captured in-the-field using a standard smart phone.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781479983391, 978-1-4799-8338-4
ISBN (Print)9781479983407
Publication statusE-pub ahead of print - 10 Dec 2015
EventInternational Conference on Image processing 2015 (ICIP 2015) - QUebec, Canada
Duration: 27 Sept 201530 Sept 2015


ConferenceInternational Conference on Image processing 2015 (ICIP 2015)


  • ecological informatics
  • log-Gabor filter
  • randomised forests
  • nearest neighbour voting


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