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Automated Wheat Disease Classification Under Controlled and Uncontrolled Image Acquisition

Siricharoen, Punnarai, Bryan, Scotney, Morrow, Philip and Parr, Gerard (2015) Automated Wheat Disease Classification Under Controlled and Uncontrolled Image Acquisition. In: UNSPECIFIED. Springer. Vol 9164 [Conference contribution]

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URL: http://dx.doi.org/10.1007/978-3-319-20801-5_50

DOI: 10.1007/978-3-319-20801-5_50


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.

Item Type:Conference contribution (UNSPECIFIED)
Keywords:Wheat disease recognition, radial pyramid, rotation using Fourier
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:37397
Deposited By: Professor Philip Morrow
Deposited On:07 Apr 2017 13:05
Last Modified:07 Apr 2017 13:05

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