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Publication - Dr David Gibson

    Towards automating visual in-field monitoring of crop health

    Citation

    Gibson, D, Burghardt, T, Campbell, N & Canagarajah, N, 2015, ‘Towards automating visual in-field monitoring of crop health’. in: 2015 IEEE International Conference on Image Processing (ICIP 2015). Institute of Electrical and Electronics Engineers (IEEE)

    Abstract

    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.

    Full details in the University publications repository