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dc.contributor.authorZulkifli, Husin
dc.contributor.authorAli Yeon, Md Shakaff, Prof. Dr.
dc.contributor.authorAbdul Halis, Abdul Aziz
dc.contributor.authorRohani, S. Mohamed Farook
dc.date.accessioned2013-07-11T06:00:23Z
dc.date.available2013-07-11T06:00:23Z
dc.date.issued2012-02
dc.identifier.citationp. 291-296en_US
dc.identifier.isbn978-076954668-1
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6169716
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/26578
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.org/en_US
dc.description.abstractProducing chili is a daunting task as the plant is exposed to the attacks from various micro-organisms and bacterial diseases and pests. The symptoms of the attacks are usually distinguished through the leaves, stems or fruit inspection. This paper discusses the effective way used in performing early detection of chili disease through leaf features inspection. Leaf image is captured and processed to determine the health status of each plant. Currently the chemicals are applied to the plants periodically without considering the requirement of each plant. This technique will ensure that the chemicals only applied when the plants are detected to be effected with the diseases. The image processing techniques are used to perform hundreds of chili disease images. The plant chili disease detection through leaf image and data processing techniques is very useful and inexpensive system especially for assisting farmers in monitoring the big plantation area.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofseriesProceedings the 3rd International Conference on Intelligent Systems Modelling and Simulation (ISMS) 2012en_US
dc.subjectChili diseaseen_US
dc.subjectImage processingen_US
dc.subjectLeaf imageen_US
dc.titleFeasibility study on plant chili disease detection using image processing techniquesen_US
dc.typeWorking Paperen_US
dc.contributor.urlzulhusin@unimap.edu.myen_US


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