Classification of paddy weed leaf using neuro-fuzzy methods / Mohd Zulhilmi Ab Jamil … [et al.]
Paddy weed appears to be one of the many visible threats to paddy crop production and subsequently farmers’ income. It is for this reason that the growth of paddy weeds in paddy fields should be controlled as it results in a significant decrease of paddy yields. However, farmers might have limited k...
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| Format: | Article |
| Language: | en |
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Universiti Teknologi MARA Press (Penerbit UiTM)
2018
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| Online Access: | https://ir.uitm.edu.my/id/eprint/43155/1/43155.pdf https://ir.uitm.edu.my/id/eprint/43155/ https://mjoc.uitm.edu.my |
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| _version_ | 1833067189255536640 |
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| author | Ab Jamil, Mohd Zulhilmi Mutalib, Sofianita Abdul-Rahman, Shuzlina Abd Aziz, Zalilah |
| author_facet | Ab Jamil, Mohd Zulhilmi Mutalib, Sofianita Abdul-Rahman, Shuzlina Abd Aziz, Zalilah |
| author_sort | Ab Jamil, Mohd Zulhilmi |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Paddy weed appears to be one of the many visible threats to paddy crop production and subsequently farmers’ income. It is for this reason that the growth of paddy weeds in paddy fields should be controlled as it results in a significant decrease of paddy yields. However, farmers might have limited knowledge on weed types, and are thus unable to identify and determine the right prevention methods. This paper presents classification methods for paddy weeds through the leaf shape extraction and applies neuro-fuzzy methods for recognizing the types of weeds. The types being focussed are the Sphenoclea zeylanica, Ludwigia hyssopifolia and Echinochloa crus-galli. The developed e-prototype methods would be able to classify paddy weeds with 83.78% accuracy. Hopefully, the findings in this study would assist farmers and researchers in increasing their paddy yields and eliminating weed growth respectively. The production of paddy in Malaysia would eventually be improved with the proposed methods, which can be considered as a technology advancement in the field of paddy production. |
| format | Article |
| id | my.uitm.ir-43155 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2018 |
| publisher | Universiti Teknologi MARA Press (Penerbit UiTM) |
| record_format | eprints |
| spelling | my.uitm.ir-431552021-03-10T06:55:49Z https://ir.uitm.edu.my/id/eprint/43155/ Classification of paddy weed leaf using neuro-fuzzy methods / Mohd Zulhilmi Ab Jamil … [et al.] mjoc Ab Jamil, Mohd Zulhilmi Mutalib, Sofianita Abdul-Rahman, Shuzlina Abd Aziz, Zalilah Expert systems (Computer science). Fuzzy expert systems Fuzzy logic Paddy weed appears to be one of the many visible threats to paddy crop production and subsequently farmers’ income. It is for this reason that the growth of paddy weeds in paddy fields should be controlled as it results in a significant decrease of paddy yields. However, farmers might have limited knowledge on weed types, and are thus unable to identify and determine the right prevention methods. This paper presents classification methods for paddy weeds through the leaf shape extraction and applies neuro-fuzzy methods for recognizing the types of weeds. The types being focussed are the Sphenoclea zeylanica, Ludwigia hyssopifolia and Echinochloa crus-galli. The developed e-prototype methods would be able to classify paddy weeds with 83.78% accuracy. Hopefully, the findings in this study would assist farmers and researchers in increasing their paddy yields and eliminating weed growth respectively. The production of paddy in Malaysia would eventually be improved with the proposed methods, which can be considered as a technology advancement in the field of paddy production. Universiti Teknologi MARA Press (Penerbit UiTM) 2018 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/43155/1/43155.pdf Classification of paddy weed leaf using neuro-fuzzy methods / Mohd Zulhilmi Ab Jamil … [et al.]. (2018) Malaysian Journal of Computing (MJoC) <https://ir.uitm.edu.my/view/publication/Malaysian_Journal_of_Computing_=28MJoC=29/>, 3 (1). pp. 54-66. ISSN 2600-8238 https://mjoc.uitm.edu.my |
| spellingShingle | Expert systems (Computer science). Fuzzy expert systems Fuzzy logic Ab Jamil, Mohd Zulhilmi Mutalib, Sofianita Abdul-Rahman, Shuzlina Abd Aziz, Zalilah Classification of paddy weed leaf using neuro-fuzzy methods / Mohd Zulhilmi Ab Jamil … [et al.] |
| title | Classification of paddy weed leaf using neuro-fuzzy methods / Mohd Zulhilmi Ab Jamil … [et al.] |
| title_full | Classification of paddy weed leaf using neuro-fuzzy methods / Mohd Zulhilmi Ab Jamil … [et al.] |
| title_fullStr | Classification of paddy weed leaf using neuro-fuzzy methods / Mohd Zulhilmi Ab Jamil … [et al.] |
| title_full_unstemmed | Classification of paddy weed leaf using neuro-fuzzy methods / Mohd Zulhilmi Ab Jamil … [et al.] |
| title_short | Classification of paddy weed leaf using neuro-fuzzy methods / Mohd Zulhilmi Ab Jamil … [et al.] |
| title_sort | classification of paddy weed leaf using neuro-fuzzy methods / mohd zulhilmi ab jamil … [et al.] |
| topic | Expert systems (Computer science). Fuzzy expert systems Fuzzy logic |
| url | https://ir.uitm.edu.my/id/eprint/43155/1/43155.pdf https://ir.uitm.edu.my/id/eprint/43155/ https://mjoc.uitm.edu.my |
| url_provider | http://ir.uitm.edu.my/ |