| dc.contributor.author | Puteh, Saad | |
| dc.contributor.author | Mohamed Rizon, Mohamed Juhari | |
| dc.contributor.author | Nor Khairah, Jamaludin | |
| dc.contributor.author | Siti Sakira, Kamarudin | |
| dc.contributor.author | Aryati, Bakri | |
| dc.contributor.author | Nursalasawati, Rusli | |
| dc.date.accessioned | 2009-07-10T07:55:28Z | |
| dc.date.available | 2009-07-10T07:55:28Z | |
| dc.date.issued | 2004-01-28 | |
| dc.identifier.citation | p.148-151 | en_US |
| dc.identifier.uri | http://dspace.unimap.edu.my/123456789/6424 | |
| dc.description | Organized by Oita University, 28th - 30th January 2004 at Beppu, Oita, Japan. | en_US |
| dc.description.abstract | Parameters that affect rice yield are many, for instance diseases, pests and weeds. Statistical or mathematical model is unable to describe the correlation between plant diseases, pests and weeds on the amount of rice yield. In this study, a Backpropogation (BP) algorithm is utilized to develop a neural network model to predict rice yield based on the aforementioned factors in MUDA irrigation area, Malaysia. The result of this study shows that the BP algorithm is able to predict the rice yield to a deviation of less than 0.21. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Oita University | en_US |
| dc.relation.ispartofseries | Proceedings of the 9th International Symposium on Artificial Life and Robotics (AROB 9th '04) | en_US |
| dc.subject | BP | en_US |
| dc.subject | Rice yield prediction | en_US |
| dc.subject | Improved unit range | en_US |
| dc.subject | Rice yield | en_US |
| dc.subject | Neural networks (Computer science) | en_US |
| dc.subject | Back propagation | en_US |
| dc.subject | Algorithms | en_US |
| dc.subject | Computer programming | en_US |
| dc.title | Backpropagation algorithm for rice yield prediction | en_US |
| dc.type | Working Paper | en_US |
