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DC Field | Value | Language |
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dc.contributor.author | Rohani S., Mohamed Farook | - |
dc.contributor.author | Abdul Halis, Abdul Aziz | - |
dc.contributor.author | Azmi, Harun | - |
dc.contributor.author | Zulkifli, Husin | - |
dc.contributor.author | Ali Yeon, Md Shakaff, Prof. Dr. | - |
dc.date.accessioned | 2013-06-25T04:39:59Z | - |
dc.date.available | 2013-06-25T04:39:59Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | p. 115-119 | en_US |
dc.identifier.isbn | 978-076954668-1 | - |
dc.identifier.uri | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6169685 | - |
dc.identifier.uri | http://dspace.unimap.edu.my/123456789/26052 | - |
dc.description | Link to publisher's homepage at http://ieeexplore.ieee.org/ | en_US |
dc.description.abstract | Yield Prediction is an essential task to be achieved in order to implement effective forward marketing. Forward marketing is a contract that will be signed between supplier and client based on the amount of delivery and the price of delivery in future. To be able to sign such a contract the supplier should be very confident that the yield could be achieved. The yield sustainability is a challenging process in agriculture. Mango cultivar Harumanis is one of the best table tropical fruit due to its aroma and sweetness. Despite its overwhelming local demand in Malaysia and also internationally, the fruit supply never meets the demand. The flowering phase is identified as an important stage as plant reproductive physiology. Currently, Harumanis mango flowering only happens once a year that restricts the yield. In this paper, data mining is used to quantify the climatic effects on Harumanis mango yield to enable yield prediction. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartofseries | Proceedings of the Intelligent Systems, Modelling and Simulation (ISMS) 2012 | en_US |
dc.subject | Data mining | en_US |
dc.subject | Mango yield prediction model | en_US |
dc.subject | Regression | en_US |
dc.subject | Soft computing | en_US |
dc.subject | Harumanis | en_US |
dc.title | Data mining on climatic factors for Harumanis mango yield prediction | en_US |
dc.type | Working Paper | en_US |
dc.contributor.url | rohani@unimap.edu.my | en_US |
Appears in Collections: | Ali Yeon Md Shakaff, Dato' Prof. Dr. Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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Data mining on climatic factors for Harumanis mango yield prediction.pdf | 26.83 kB | Adobe PDF | View/Open |
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