Automated wheat disease classification under controlled and uncontrolled image acquisition

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Siricharoen, Punnarai, Scotney, Bryan, Morrow, Philip and Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132 (2015) Automated wheat disease classification under controlled and uncontrolled image acquisition. In: International Conference Image Analysis and Recognition. Springer, pp. 456-464. ISBN 978-3-319-20800-8

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Abstract

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 diseased 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 conditions, respectively.

Item Type: Book Section
Uncontrolled Keywords: radial pyramid,rotation using fourier,wheat disease recognition,theoretical computer science,computer science(all) ,/dk/atira/pure/subjectarea/asjc/2600/2614
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Smart Emerging Technologies
Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory
Faculty of Science > Research Groups > Data Science and AI
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Depositing User: Pure Connector
Date Deposited: 08 Nov 2016 16:00
Last Modified: 10 Dec 2024 01:11
URI: https://ueaeprints.uea.ac.uk/id/eprint/61271
DOI: 10.1007/978-3-319-20801-5_50

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