A lightweight mobile system for crop disease diagnosis

Siricharoen, P., Scotney, B., Morrow, P. and Parr, G. ORCID: https://orcid.org/0000-0002-9365-9132 (2016) A lightweight mobile system for crop disease diagnosis. Lecture Notes in Computer Science, 9730. pp. 783-791. ISSN 0302-9743

Full text not available from this repository. (Request a copy)


This paper presents a low-complexity mobile application for automatically diagnosing crop diseases in the field. In an initial pre-processing stage, the system leverages the capability of a smartphone device and basic image processing algorithms to obtain consistent leaf orientation and to remove the background. A number of different features are then extracted from the leaf, including texture, colour and shape features. Nine lightweight sub-features are combined and implemented as a feature descriptor for this mobile environment. The system is applied to six wheat leaf types: non-disease, yellow rust, Septoria, brown rust, powdery mildew and tan spots, which are commonly occurring wheat diseases worldwide. The standalone application demonstrates the possibilities for disease diagnosis under realistic circumstances, with disease/non-disease detection accuracy of approximately 88 %, and can provide a possible disease type within a few seconds of image acquisition.

Item Type: Article
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
Related URLs:
Depositing User: Pure Connector
Date Deposited: 24 Sep 2016 00:31
Last Modified: 14 Mar 2023 08:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/60080
DOI: 10.1007/978-3-319-41501-7_87

Actions (login required)

View Item View Item