Paddy leaf disease recognition system using image processing techniques and support vector machine / Hayatun Syamila Mamat, Nur Azwani Zaini and Suhaila Abd Halim … [et al.]
Paddy is a crucial agroculture sector since rice is the staple food for the majority of the world's population. However, the production of paddy is slower and less productive since many factors have affected the growth of the paddy. The existence of disease in paddy component affects the qualit...
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Universiti Teknologi MARA Cawangan Pulau Pinang
2020
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| Online Access: | https://ir.uitm.edu.my/id/eprint/40647/1/40647.pdf https://ir.uitm.edu.my/id/eprint/40647/ https://uppp.uitm.edu.my |
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| author | Mamat, Hayatun Syamila Zaini, Nur Azwani Abd Halim, Suhaila |
| author_facet | Mamat, Hayatun Syamila Zaini, Nur Azwani Abd Halim, Suhaila |
| author_sort | Mamat, Hayatun Syamila |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Paddy is a crucial agroculture sector since rice is the staple food for the majority of the world's population. However, the production of paddy is slower and less productive since many factors have affected the growth of the paddy. The existence of disease in paddy component affects the quality of rice produced. Hence, the recognition of the disease at the beginning stage is crucial as the initial approach for prevention purposes. In this study, a system is developed to detect the paddy leaf disease such as bacterial leaf blight, brown spot and leaf smut. All the processes involved are implemented and compiled using MATLAB R2020a. A set of 105 image data with disease is converted to binary image using thresholding. 6 features from all the data are extracted and divided to testing and training set before the classification process. A cubic support vector machine is used for the classification process. Lastly, accuracy, precision, and misclassification for each disease are calculated for performance evaluation. Results show that the average performance of the diseases on accuracy, precision, and misclassification are 88.57%, 82.97%, and 11.43% respectively. The use of the processes act as assistance to the paddy farmer to identify the existence of the paddy leaf disease. This could improve the quality of the paddy produced by reducing the process of manual disease checking. |
| format | Article |
| id | my.uitm.ir-40647 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2020 |
| publisher | Universiti Teknologi MARA Cawangan Pulau Pinang |
| record_format | eprints |
| spelling | my.uitm.ir-406472025-08-22T08:52:47Z https://ir.uitm.edu.my/id/eprint/40647/ Paddy leaf disease recognition system using image processing techniques and support vector machine / Hayatun Syamila Mamat, Nur Azwani Zaini and Suhaila Abd Halim … [et al.] esteem Mamat, Hayatun Syamila Zaini, Nur Azwani Abd Halim, Suhaila Regression analysis. Correlation analysis. Spatial analysis (Statistics) System design Paddy is a crucial agroculture sector since rice is the staple food for the majority of the world's population. However, the production of paddy is slower and less productive since many factors have affected the growth of the paddy. The existence of disease in paddy component affects the quality of rice produced. Hence, the recognition of the disease at the beginning stage is crucial as the initial approach for prevention purposes. In this study, a system is developed to detect the paddy leaf disease such as bacterial leaf blight, brown spot and leaf smut. All the processes involved are implemented and compiled using MATLAB R2020a. A set of 105 image data with disease is converted to binary image using thresholding. 6 features from all the data are extracted and divided to testing and training set before the classification process. A cubic support vector machine is used for the classification process. Lastly, accuracy, precision, and misclassification for each disease are calculated for performance evaluation. Results show that the average performance of the diseases on accuracy, precision, and misclassification are 88.57%, 82.97%, and 11.43% respectively. The use of the processes act as assistance to the paddy farmer to identify the existence of the paddy leaf disease. This could improve the quality of the paddy produced by reducing the process of manual disease checking. Universiti Teknologi MARA Cawangan Pulau Pinang 2020-12 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/40647/1/40647.pdf Mamat, Hayatun Syamila and Zaini, Nur Azwani and Abd Halim, Suhaila (2020) Paddy leaf disease recognition system using image processing techniques and support vector machine / Hayatun Syamila Mamat, Nur Azwani Zaini and Suhaila Abd Halim … [et al.]. (2020) ESTEEM Academic Journal <https://ir.uitm.edu.my/view/publication/ESTEEM_Academic_Journal.html>, 16 (Dec). pp. 41-50. ISSN 2289-4934 https://uppp.uitm.edu.my |
| spellingShingle | Regression analysis. Correlation analysis. Spatial analysis (Statistics) System design Mamat, Hayatun Syamila Zaini, Nur Azwani Abd Halim, Suhaila Paddy leaf disease recognition system using image processing techniques and support vector machine / Hayatun Syamila Mamat, Nur Azwani Zaini and Suhaila Abd Halim … [et al.] |
| title | Paddy leaf disease recognition system using image processing techniques and support vector machine / Hayatun Syamila Mamat, Nur Azwani Zaini and Suhaila Abd Halim … [et al.] |
| title_full | Paddy leaf disease recognition system using image processing techniques and support vector machine / Hayatun Syamila Mamat, Nur Azwani Zaini and Suhaila Abd Halim … [et al.] |
| title_fullStr | Paddy leaf disease recognition system using image processing techniques and support vector machine / Hayatun Syamila Mamat, Nur Azwani Zaini and Suhaila Abd Halim … [et al.] |
| title_full_unstemmed | Paddy leaf disease recognition system using image processing techniques and support vector machine / Hayatun Syamila Mamat, Nur Azwani Zaini and Suhaila Abd Halim … [et al.] |
| title_short | Paddy leaf disease recognition system using image processing techniques and support vector machine / Hayatun Syamila Mamat, Nur Azwani Zaini and Suhaila Abd Halim … [et al.] |
| title_sort | paddy leaf disease recognition system using image processing techniques and support vector machine / hayatun syamila mamat, nur azwani zaini and suhaila abd halim … [et al.] |
| topic | Regression analysis. Correlation analysis. Spatial analysis (Statistics) System design |
| url | https://ir.uitm.edu.my/id/eprint/40647/1/40647.pdf https://ir.uitm.edu.my/id/eprint/40647/ https://uppp.uitm.edu.my |
| url_provider | http://ir.uitm.edu.my/ |