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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/579074
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dc.contributor.authorA. Che Soh (UPM)
dc.contributor.authorU. K. Mohamad Yusof (UPM)
dc.contributor.authorN. F. M. Radzi (UPM)
dc.contributor.authorA. J. Ishak (UPM)
dc.contributor.authorM. K. Hassan (UPM)
dc.date.accessioned2023-11-06T03:14:03Z-
dc.date.available2023-11-06T03:14:03Z-
dc.date.issued2017-01
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116435
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/579074-
dc.descriptionHerbs have unique characteristics such as colour, texture and odour. In general, herb identification is through organoleptic methods and is heavily dependent on botanists. It is becoming more difficult to identify different herb species in the same family based only on their aroma . It is because of their similar physical appearance and smell. Artificial technology, unlike humans, is thought to have the capacity to identify different species with precision. An instrument used to identify aroma is the electronic nose. It is used in many sector including agriculture. The electronic nose in this project was to identify the odour of 12 species such as lauraceae, myrtaceae and zingiberaceae families. The output captured by the electronic nose gas sensors were classified using two types of artificial intelligent techniques: Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). From the result, ANFIS has 94.8% accuracy compared with ANN at 91.7%.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-25-S-1
dc.rightsUKM
dc.subjectArtificial Neural Network
dc.subjectAdaptive Neuro-Fuzzy Inference System
dc.titleClassification of aromatic herbs using artificial intelligent technique
dc.typeJournal Article
dc.format.volume25
dc.format.pages119-128
dc.format.issueSpecial Issue
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