Development of a Rice Plant Disease Classification Model in Big Data Environment
More than the half of the global population consume rice as their primary energy source. Therefore, this work focused on the development of a prediction model to minimize agricultural loss in the paddy field. Initially, rice plant diseases, along with their images, were captured. Then, a big data fr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/12/758 |
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| author | Shampa Sengupta Abhijit Dutta Shaimaa A. M. Abdelmohsen Haifa A. Alyousef Mohammad Rahimi-Gorji |
| author_facet | Shampa Sengupta Abhijit Dutta Shaimaa A. M. Abdelmohsen Haifa A. Alyousef Mohammad Rahimi-Gorji |
| author_sort | Shampa Sengupta |
| collection | DOAJ |
| description | More than the half of the global population consume rice as their primary energy source. Therefore, this work focused on the development of a prediction model to minimize agricultural loss in the paddy field. Initially, rice plant diseases, along with their images, were captured. Then, a big data framework was used to encounter a large dataset. In this work, at first, feature extraction process is applied on the data and after that feature selection is also applied to obtain the reduced data with important features which is used as the input to the classification model. For the rice disease datasets, features based on color, shape, position, and texture are extracted from the infected rice plant images and a rough set theory-based feature selection method is used for the feature selection job. For the classification task, ensemble classification methods have been implemented in a map reduce framework for the development of the efficient disease prediction model. The results on the collected disease data show the efficiency of the proposed model. |
| first_indexed | 2024-03-09T17:19:09Z |
| format | Article |
| id | doaj.art-58c64f0956a64753b0a3700f84b74886 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T17:19:09Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-58c64f0956a64753b0a3700f84b748862023-11-24T13:20:30ZengMDPI AGBioengineering2306-53542022-12-0191275810.3390/bioengineering9120758Development of a Rice Plant Disease Classification Model in Big Data EnvironmentShampa Sengupta0Abhijit Dutta1Shaimaa A. M. Abdelmohsen2Haifa A. Alyousef3Mohammad Rahimi-Gorji4Department of Information Technology, MCKV Institute of Engineering, Liluah, Howrah 711204, IndiaDepartment of Mechanical Engineering, MCKV Institute of Engineering, Liluah, Howrah 711204, IndiaDepartment of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaFaculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, BelgiumMore than the half of the global population consume rice as their primary energy source. Therefore, this work focused on the development of a prediction model to minimize agricultural loss in the paddy field. Initially, rice plant diseases, along with their images, were captured. Then, a big data framework was used to encounter a large dataset. In this work, at first, feature extraction process is applied on the data and after that feature selection is also applied to obtain the reduced data with important features which is used as the input to the classification model. For the rice disease datasets, features based on color, shape, position, and texture are extracted from the infected rice plant images and a rough set theory-based feature selection method is used for the feature selection job. For the classification task, ensemble classification methods have been implemented in a map reduce framework for the development of the efficient disease prediction model. The results on the collected disease data show the efficiency of the proposed model.https://www.mdpi.com/2306-5354/9/12/758data miningbig datarough set theoryensemble classificationrice disease prediction |
| spellingShingle | Shampa Sengupta Abhijit Dutta Shaimaa A. M. Abdelmohsen Haifa A. Alyousef Mohammad Rahimi-Gorji Development of a Rice Plant Disease Classification Model in Big Data Environment Bioengineering data mining big data rough set theory ensemble classification rice disease prediction |
| title | Development of a Rice Plant Disease Classification Model in Big Data Environment |
| title_full | Development of a Rice Plant Disease Classification Model in Big Data Environment |
| title_fullStr | Development of a Rice Plant Disease Classification Model in Big Data Environment |
| title_full_unstemmed | Development of a Rice Plant Disease Classification Model in Big Data Environment |
| title_short | Development of a Rice Plant Disease Classification Model in Big Data Environment |
| title_sort | development of a rice plant disease classification model in big data environment |
| topic | data mining big data rough set theory ensemble classification rice disease prediction |
| url | https://www.mdpi.com/2306-5354/9/12/758 |
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