Selection of soil features for detection of ganoderma using rough set theory
Ganoderma boninense (G. boninense) is one of the critical palm oil diseases that have caused major loss in palm oil production, especially in Malaysia. Current detection methods are based on molecular and non-molecular approaches. Unfortunately, both are expensive and time consuming. Meanwhile, wire...
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| Format: | Conference or Workshop Item |
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2015
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| _version_ | 1796860716475482112 |
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| author | Mohd. Zamry, Nurfazrina Zainal, Anazida A. Rassam, Murad Bakhtiari, Majid Maarof, Mohd. Aizaini |
| author_facet | Mohd. Zamry, Nurfazrina Zainal, Anazida A. Rassam, Murad Bakhtiari, Majid Maarof, Mohd. Aizaini |
| author_sort | Mohd. Zamry, Nurfazrina |
| collection | ePrints |
| description | Ganoderma boninense (G. boninense) is one of the critical palm oil diseases that have caused major loss in palm oil production, especially in Malaysia. Current detection methods are based on molecular and non-molecular approaches. Unfortunately, both are expensive and time consuming. Meanwhile, wireless sensor networks (WSNs) have been successfully used in precision agriculture and have a potential to be deployed in palm oil plantation. The success of using WSN to detect anomalous events in other domain reaffirms that WSN could be used to detect the presence of G. boninense, since WSN has some resource constraints such as energy and memory. This paper focuses on feature selection to ensure only significant and relevant data that will be collected and transmitted by the sensor nodes. Sixteen soil features have been collected from the palm oil plantation. This research used rough set technique to do feature selection. Few algorithms were compared in terms of their classification accuracy, and we found that genetic algorithm gave the best combination of feature subset to signify the presence of Ganoderma in soil. |
| first_indexed | 2024-03-05T19:45:27Z |
| format | Conference or Workshop Item |
| id | utm.eprints-59466 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:45:27Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | utm.eprints-594662021-12-19T04:42:27Z http://eprints.utm.my/59466/ Selection of soil features for detection of ganoderma using rough set theory Mohd. Zamry, Nurfazrina Zainal, Anazida A. Rassam, Murad Bakhtiari, Majid Maarof, Mohd. Aizaini QA75 Electronic computers. Computer science Ganoderma boninense (G. boninense) is one of the critical palm oil diseases that have caused major loss in palm oil production, especially in Malaysia. Current detection methods are based on molecular and non-molecular approaches. Unfortunately, both are expensive and time consuming. Meanwhile, wireless sensor networks (WSNs) have been successfully used in precision agriculture and have a potential to be deployed in palm oil plantation. The success of using WSN to detect anomalous events in other domain reaffirms that WSN could be used to detect the presence of G. boninense, since WSN has some resource constraints such as energy and memory. This paper focuses on feature selection to ensure only significant and relevant data that will be collected and transmitted by the sensor nodes. Sixteen soil features have been collected from the palm oil plantation. This research used rough set technique to do feature selection. Few algorithms were compared in terms of their classification accuracy, and we found that genetic algorithm gave the best combination of feature subset to signify the presence of Ganoderma in soil. 2015 Conference or Workshop Item PeerReviewed Mohd. Zamry, Nurfazrina and Zainal, Anazida and A. Rassam, Murad and Bakhtiari, Majid and Maarof, Mohd. Aizaini (2015) Selection of soil features for detection of ganoderma using rough set theory. In: 4th World Congress on Information and Communication Technologies, WICT 2014, 8 December 2014 - 11 December 2014, Melaka, Malaysia. http://dx.doi.org/10.1007/978-3-319-17398-6_28 |
| spellingShingle | QA75 Electronic computers. Computer science Mohd. Zamry, Nurfazrina Zainal, Anazida A. Rassam, Murad Bakhtiari, Majid Maarof, Mohd. Aizaini Selection of soil features for detection of ganoderma using rough set theory |
| title | Selection of soil features for detection of ganoderma using rough set theory |
| title_full | Selection of soil features for detection of ganoderma using rough set theory |
| title_fullStr | Selection of soil features for detection of ganoderma using rough set theory |
| title_full_unstemmed | Selection of soil features for detection of ganoderma using rough set theory |
| title_short | Selection of soil features for detection of ganoderma using rough set theory |
| title_sort | selection of soil features for detection of ganoderma using rough set theory |
| topic | QA75 Electronic computers. Computer science |
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