Classification of aromatic herbs using artificial intelligent technique
Herbs 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...
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| Format: | Article |
| Language: | English |
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Universiti Putra Malaysia Press
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/55827/ http://psasir.upm.edu.my/id/eprint/55827/1/14-JTS%28S%29-0090-2016-4thProof.pdf |
| _version_ | 1848852911221637120 |
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| author | Che Soh, Azura Mohamad Yusof, Umi Kalsom Mohamad Radzi, Nur Fadzilah Ishak, Asnor Juraiza Hassan, Mohd Khair |
| author_facet | Che Soh, Azura Mohamad Yusof, Umi Kalsom Mohamad Radzi, Nur Fadzilah Ishak, Asnor Juraiza Hassan, Mohd Khair |
| author_sort | Che Soh, Azura |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Herbs 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%. |
| first_indexed | 2025-11-15T10:45:36Z |
| format | Article |
| id | upm-55827 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:45:36Z |
| publishDate | 2017 |
| publisher | Universiti Putra Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-558272017-07-05T04:57:43Z http://psasir.upm.edu.my/id/eprint/55827/ Classification of aromatic herbs using artificial intelligent technique Che Soh, Azura Mohamad Yusof, Umi Kalsom Mohamad Radzi, Nur Fadzilah Ishak, Asnor Juraiza Hassan, Mohd Khair Herbs 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%. Universiti Putra Malaysia Press 2017 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/55827/1/14-JTS%28S%29-0090-2016-4thProof.pdf Che Soh, Azura and Mohamad Yusof, Umi Kalsom and Mohamad Radzi, Nur Fadzilah and Ishak, Asnor Juraiza and Hassan, Mohd Khair (2017) Classification of aromatic herbs using artificial intelligent technique. Pertanika Journal of Science & Technology, 25 (spec. Jan.). pp. 119-128. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2025%20(S)%20Jan.%202017/14-JTS(S)-0090-2016-4thProof.pdf |
| spellingShingle | Che Soh, Azura Mohamad Yusof, Umi Kalsom Mohamad Radzi, Nur Fadzilah Ishak, Asnor Juraiza Hassan, Mohd Khair Classification of aromatic herbs using artificial intelligent technique |
| title | Classification of aromatic herbs using artificial intelligent technique |
| title_full | Classification of aromatic herbs using artificial intelligent technique |
| title_fullStr | Classification of aromatic herbs using artificial intelligent technique |
| title_full_unstemmed | Classification of aromatic herbs using artificial intelligent technique |
| title_short | Classification of aromatic herbs using artificial intelligent technique |
| title_sort | classification of aromatic herbs using artificial intelligent technique |
| url | http://psasir.upm.edu.my/id/eprint/55827/ http://psasir.upm.edu.my/id/eprint/55827/ http://psasir.upm.edu.my/id/eprint/55827/1/14-JTS%28S%29-0090-2016-4thProof.pdf |