Rice yield classification using backpropagation network
Among factors that affect rice yield are diseases, pests and weeds. It is intractable to model the correlation between plant diseases, pests and weeds on the amount of rice yield statistically and mathematically. In this study, a backpropagation network (BPN) is developed to classify rice yield base...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia
2004
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/1043/1/P._Saad.pdf |
| _version_ | 1803624221857808384 |
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| author | Saad, P. Jamaludin, N.K. Kamarudin, S. S. Rusli, N. |
| author_facet | Saad, P. Jamaludin, N.K. Kamarudin, S. S. Rusli, N. |
| author_sort | Saad, P. |
| collection | UUM |
| description | Among factors that affect rice yield are diseases, pests and weeds. It is intractable to model the correlation between plant diseases, pests and weeds on the amount of rice yield statistically and mathematically. In this study, a backpropagation network (BPN) is developed to classify rice yield based on the aforementioned factors in MUDA irrigation area Malaysia. The result of this study shows that BPN is able to classify the rice yield to a deviation of 0.03.
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| first_indexed | 2024-07-04T05:14:56Z |
| format | Article |
| id | uum-1043 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:14:56Z |
| publishDate | 2004 |
| publisher | Universiti Utara Malaysia |
| record_format | dspace |
| spelling | uum-10432010-09-05T04:55:31Z https://repo.uum.edu.my/id/eprint/1043/ Rice yield classification using backpropagation network Saad, P. Jamaludin, N.K. Kamarudin, S. S. Rusli, N. QA75 Electronic computers. Computer science Among factors that affect rice yield are diseases, pests and weeds. It is intractable to model the correlation between plant diseases, pests and weeds on the amount of rice yield statistically and mathematically. In this study, a backpropagation network (BPN) is developed to classify rice yield based on the aforementioned factors in MUDA irrigation area Malaysia. The result of this study shows that BPN is able to classify the rice yield to a deviation of 0.03. Universiti Utara Malaysia 2004 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/1043/1/P._Saad.pdf Saad, P. and Jamaludin, N.K. and Kamarudin, S. S. and Rusli, N. (2004) Rice yield classification using backpropagation network. Journal of ICT, 3 (1). pp. 67-81. ISSN 1675-414X http://jict.uum.edu.my |
| spellingShingle | QA75 Electronic computers. Computer science Saad, P. Jamaludin, N.K. Kamarudin, S. S. Rusli, N. Rice yield classification using backpropagation network |
| title | Rice yield classification using backpropagation network |
| title_full | Rice yield classification using backpropagation network |
| title_fullStr | Rice yield classification using backpropagation network |
| title_full_unstemmed | Rice yield classification using backpropagation network |
| title_short | Rice yield classification using backpropagation network |
| title_sort | rice yield classification using backpropagation network |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/1043/1/P._Saad.pdf |
| work_keys_str_mv | AT saadp riceyieldclassificationusingbackpropagationnetwork AT jamaludinnk riceyieldclassificationusingbackpropagationnetwork AT kamarudinss riceyieldclassificationusingbackpropagationnetwork AT ruslin riceyieldclassificationusingbackpropagationnetwork |