Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming
Food security challenges in Southeast Asia, across all income brackets, have been growing, according to the Food and Agriculture Organization (FAO) of the United Nations. This article introduced innovative Artificial Intelligence-based (AI-based) predictive algorithms for short-term rice production,...
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Tun Hussein Onn
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43868/ http://umpir.ump.edu.my/id/eprint/43868/1/MARI-Vol.%206%20Issue%202%20%282025%29.pdf |
| _version_ | 1848826978585542656 |
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| author | Chuan, Zun Liang Tham, Ren Sheng Tan, Chek Cheng Abraham Lim, Bing Sern Chong, Yeh Sai |
| author_facet | Chuan, Zun Liang Tham, Ren Sheng Tan, Chek Cheng Abraham Lim, Bing Sern Chong, Yeh Sai |
| author_sort | Chuan, Zun Liang |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Food security challenges in Southeast Asia, across all income brackets, have been growing, according to the Food and Agriculture Organization (FAO) of the United Nations. This article introduced innovative Artificial Intelligence-based (AI-based) predictive algorithms for short-term rice production, utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) data science framework. The predictive algorithms integrated features addressing three food security dimensions: availability, accessibility, and stability, and identified key determinants in three clusters: atmospheric, socioeconomic, and farming practices. By employing the proposed innovative modified stacked Multiple Linear Regression-Support Vector Regression-based (MLR-SVR-based) algorithms, and ranking them utilizing the modified Taguchi-based VIseKriterijumska Optimizacija I Kompromisno Resenje (Taguchi-based VIKOR) multi-criteria decision-making algorithm, the analysis demonstrated high predictive accuracy even with limited data. The proposed AI-based predictive algorithm was utilized to forecast 5-year future rice production for Southeast Asia nations, yielding generally accurate results except for Cambodia (KHM). This research has significant implications for agriculture, food production, data analytics, and policymaking, potentially enhancing efficiency and innovation in agricultural operations. |
| first_indexed | 2025-11-15T03:53:24Z |
| format | Article |
| id | ump-43868 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:53:24Z |
| publishDate | 2025 |
| publisher | Penerbit Universiti Tun Hussein Onn |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-438682025-02-20T02:07:36Z http://umpir.ump.edu.my/id/eprint/43868/ Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming Chuan, Zun Liang Tham, Ren Sheng Tan, Chek Cheng Abraham Lim, Bing Sern Chong, Yeh Sai HA Statistics QA Mathematics S Agriculture (General) Food security challenges in Southeast Asia, across all income brackets, have been growing, according to the Food and Agriculture Organization (FAO) of the United Nations. This article introduced innovative Artificial Intelligence-based (AI-based) predictive algorithms for short-term rice production, utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) data science framework. The predictive algorithms integrated features addressing three food security dimensions: availability, accessibility, and stability, and identified key determinants in three clusters: atmospheric, socioeconomic, and farming practices. By employing the proposed innovative modified stacked Multiple Linear Regression-Support Vector Regression-based (MLR-SVR-based) algorithms, and ranking them utilizing the modified Taguchi-based VIseKriterijumska Optimizacija I Kompromisno Resenje (Taguchi-based VIKOR) multi-criteria decision-making algorithm, the analysis demonstrated high predictive accuracy even with limited data. The proposed AI-based predictive algorithm was utilized to forecast 5-year future rice production for Southeast Asia nations, yielding generally accurate results except for Cambodia (KHM). This research has significant implications for agriculture, food production, data analytics, and policymaking, potentially enhancing efficiency and innovation in agricultural operations. Penerbit Universiti Tun Hussein Onn 2025-02-19 Article PeerReviewed pdf en cc_by_nc_sa_4 http://umpir.ump.edu.my/id/eprint/43868/1/MARI-Vol.%206%20Issue%202%20%282025%29.pdf Chuan, Zun Liang and Tham, Ren Sheng and Tan, Chek Cheng and Abraham Lim, Bing Sern and Chong, Yeh Sai (2025) Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming. Multidisciplinary Applied Research and Innovation, 6 (2). pp. 1-17. ISSN 2773-4773. (Published) https://publisher.uthm.edu.my/periodicals/index.php/mari/article/view/19630 |
| spellingShingle | HA Statistics QA Mathematics S Agriculture (General) Chuan, Zun Liang Tham, Ren Sheng Tan, Chek Cheng Abraham Lim, Bing Sern Chong, Yeh Sai Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming |
| title | Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming |
| title_full | Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming |
| title_fullStr | Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming |
| title_full_unstemmed | Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming |
| title_short | Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming |
| title_sort | smart agriculture economics and engineering: unveiling the innovation behind ai-enhanced rice farming |
| topic | HA Statistics QA Mathematics S Agriculture (General) |
| url | http://umpir.ump.edu.my/id/eprint/43868/ http://umpir.ump.edu.my/id/eprint/43868/ http://umpir.ump.edu.my/id/eprint/43868/1/MARI-Vol.%206%20Issue%202%20%282025%29.pdf |