Machine Learning-Based Analysis of Paddy Crop Conditions
Malaysia, heavily reliant on rice as a staple food, faces challenges in ensuring sufficient supply due to the persistent issue of plant diseases affecting productivity. Despite being the 22nd largest rice producer in Asia, the country imports 30 to 40 percent of its annual consumption, totaling...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2023
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1834/ http://eprints.intimal.edu.my/1834/1/ij2023_66r.pdf http://eprints.intimal.edu.my/1834/2/131 |
| _version_ | 1848766966279438336 |
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| author | Teo, Xiao Hui Lim, Shu Ting Goh, Ching Pang |
| author_facet | Teo, Xiao Hui Lim, Shu Ting Goh, Ching Pang |
| author_sort | Teo, Xiao Hui |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Malaysia, heavily reliant on rice as a staple food, faces challenges in ensuring sufficient supply
due to the persistent issue of plant diseases affecting productivity. Despite being the 22nd largest
rice producer in Asia, the country imports 30 to 40 percent of its annual consumption, totaling 2.7
million tonnes. While Kedah and Perlis contribute significantly to local production, overall output
falls short of meeting demand. The government aims to enhance productivity for self-sufficiency
and cost reduction. Plant diseases, including brown spots and leaf blasts, hinder rice growth,
leading to yield loss. Current manual detection methods prove costly, inefficient, and prone to
errors. A shift toward innovative, automated solutions is imperative to address these challenges
and secure the stability of Malaysia's rice supply. This research will apply three machine learning
algorithms which are support vector machine (SVM), logistic regression (LR) and random forest
(RF) to predict the paddy conditions based on the physical appearances. The result shows that the
RF has better performance on the accuracy score of 83% |
| first_indexed | 2025-11-14T11:57:39Z |
| format | Article |
| id | intimal-1834 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:59:32Z |
| publishDate | 2023 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-18342025-07-24T09:02:59Z http://eprints.intimal.edu.my/1834/ Machine Learning-Based Analysis of Paddy Crop Conditions Teo, Xiao Hui Lim, Shu Ting Goh, Ching Pang QA76 Computer software T Technology (General) Malaysia, heavily reliant on rice as a staple food, faces challenges in ensuring sufficient supply due to the persistent issue of plant diseases affecting productivity. Despite being the 22nd largest rice producer in Asia, the country imports 30 to 40 percent of its annual consumption, totaling 2.7 million tonnes. While Kedah and Perlis contribute significantly to local production, overall output falls short of meeting demand. The government aims to enhance productivity for self-sufficiency and cost reduction. Plant diseases, including brown spots and leaf blasts, hinder rice growth, leading to yield loss. Current manual detection methods prove costly, inefficient, and prone to errors. A shift toward innovative, automated solutions is imperative to address these challenges and secure the stability of Malaysia's rice supply. This research will apply three machine learning algorithms which are support vector machine (SVM), logistic regression (LR) and random forest (RF) to predict the paddy conditions based on the physical appearances. The result shows that the RF has better performance on the accuracy score of 83% INTI International University 2023-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1834/1/ij2023_66r.pdf text en cc_by_4 http://eprints.intimal.edu.my/1834/2/131 Teo, Xiao Hui and Lim, Shu Ting and Goh, Ching Pang (2023) Machine Learning-Based Analysis of Paddy Crop Conditions. INTI JOURNAL, 2023 (66). pp. 1-6. ISSN e2600-7320 https://intijournal.intimal.edu.my |
| spellingShingle | QA76 Computer software T Technology (General) Teo, Xiao Hui Lim, Shu Ting Goh, Ching Pang Machine Learning-Based Analysis of Paddy Crop Conditions |
| title | Machine Learning-Based Analysis of Paddy Crop Conditions |
| title_full | Machine Learning-Based Analysis of Paddy Crop Conditions |
| title_fullStr | Machine Learning-Based Analysis of Paddy Crop Conditions |
| title_full_unstemmed | Machine Learning-Based Analysis of Paddy Crop Conditions |
| title_short | Machine Learning-Based Analysis of Paddy Crop Conditions |
| title_sort | machine learning-based analysis of paddy crop conditions |
| topic | QA76 Computer software T Technology (General) |
| url | http://eprints.intimal.edu.my/1834/ http://eprints.intimal.edu.my/1834/ http://eprints.intimal.edu.my/1834/1/ij2023_66r.pdf http://eprints.intimal.edu.my/1834/2/131 |