Automated Wheat Disease Classification Under Controlled and Uncontrolled Image Acquisition

Punnarai Siricharoen, Scotney Bryan, Philip Morrow, Gerard Parr

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)
119 Downloads (Pure)


This paper presents a practical classification system for recognising diseased wheat leaves and consists of a number of components. Pre-processing is performed to adjust the orientation of the primary leaf in the image using a Fourier Transform. A Wavelet Transform is then applied to partially remove low frequency information or background in the image. Subsequently, the dis- eased regions of the primary leaf are segmented out as blobs using Otsu’s thresholding. The disease blobs are normalised and then radially partitioned into sub-regions (using a Radial Pyramid) representing radial development of many diseases. Finally, global features are computed for different pyramid layers and combined to create a feature descriptor for training a linear SVM classifier. The system is evaluated by classifying three types of wheat leaf disease: non- diseased, Yellow Rust and Septoria. The classification accuracies are slightly over 95% and 79% for images captured under controlled and uncontrolled con- ditions, respectively.
Original languageEnglish
Title of host publicationUnknown Host Publication
Publication statusPublished (in print/issue) - 4 Jul 2015


  • Wheat disease recognition
  • radial pyramid
  • rotation using Fourier


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