Artificial Technology (AI) Development in Argiculture Sector in Malaysia

This paper examines the development and adoption of Artificial Intelligence (AI) in the Malaysian agriculture sector through a structured narrative conceptual review. The paper is not an empirical survey; instead, it synthesises peer-reviewed research, Malaysian government and institutional documents, Food and Agriculture Organization (FAO) publications, and relevant technology-adoption literature to develop and explain a conceptual framework for AI adoption and agricultural outcomes. The study retains the authors’ original conceptual model, in which AI Technology Adoption is the independent variable and Agricultural Productivity and Efficiency is the dependent variable. AI Literacy and Education, Training and Support, and Culture and Labour are retained as mediating variables; Government Policies and Support is retained as a moderating variable; and Farm Size and Resources and Market Demand are retained as control variables. The framework is theoretically supported by the Technology–Organization–Environment perspective, Diffusion of Innovations, and the Unified Theory of Acceptance and Use of Technology. The review shows that AI and related digital technologies can support precision agriculture, crop monitoring, input management, aquaculture, automation and supply-chain activities, but the benefits of adoption depend on human capability, training, labour conditions, infrastructure, financial resources, market incentives and government support. Malaysian evidence includes farmer acceptance of agricultural drones, MARDI precision and digital technologies, and national policy initiatives that identify agriculture as a priority area for AI application. The paper distinguishes existing Malaysian applications from emerging and potential applications and discusses socioeconomic implications including changing labour requirements, digital inequality and possible smallholder exclusion. The review concludes that AI should be approached as a socio-technical transformation in which technology adoption is translated into productivity and efficiency through human, institutional and market conditions. The conceptual framework provides a basis for future empirical testing using Malaysian farm-level data.

Artificial Intelligence (AI), Food Security, Agricultural Productivity, Sustainable Agriculture, Smart Farming

1. Abdullah, N., Nawi, N. M., Iskandar, M. M., Lazim, R. M., & Masroon, M. H. (2020). Adoption of IR4.0 into agricultural sector in Malaysia: Potential and challenges. [Google Scholar] [Crossref]

2. Azizul, A. S., El Pebrian, D., Mustaffha, S., Shamsi, S. M., Zahari, M. K., & Ruslan, N. A. (2023). The use of drone for rice cultivation in Malaysia: Identification of factors influencing its farmers’ acceptance. Journal of the Saudi Society of Agricultural Sciences, 22(7), 461–468. https://doi.org/10.1016/j.jssas.2023.04.005 [Google Scholar] [Crossref]

3. Charlton, D., Hill, A. E., & Taylor, J. E. (2022). Automation and social impacts: Winners and losers. FAO Agricultural Development Economics Working Paper 22-09. Food and Agriculture Organization of the United Nations. [Google Scholar] [Crossref]

4. Department of Statistics Malaysia. (2025). Selected agricultural indicators, Malaysia, 2024. Putrajaya: DOSM. [Google Scholar] [Crossref]

5. Food and Agriculture Organization of the United Nations. (2022). The State of Food and Agriculture 2022: Leveraging agricultural automation for transforming agrifood systems. Rome: FAO. [Google Scholar] [Crossref]

6. Lada, S., Chekima, B., Abdul Karim, M. R., Fabeil, N. F., Ayub, M. S., Amirul, S. M., Ansar, R., Bouteraa, M., Fook, L. M., & Zaki, H. O. (2023). Determining factors related to artificial intelligence (AI) adoption among Malaysia’s small and medium-sized businesses. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 100144. https://doi.org/10.1016/j.joitmc.2023.100144 [Google Scholar] [Crossref]

7. Malaysian Agricultural Research and Development Institute. (2024). Comparison of tebuconazole and trifloxystrobin residue levels on paddy grains and paddy straw harvested from paddy fields applied with knapsack sprayer, motorised knapsack sprayer or drone application. Journal of Tropical Agriculture and Food Science, 52(2), 13–17. [Google Scholar] [Crossref]

8. Malaysian Agricultural Research and Development Institute. (2025). MARDI technology: Smart fertigation system based on the Internet of Things, variable-rate seed sowing system, RiceFert-OC and vertical vegetable growing system. [Google Scholar] [Crossref]

9. McCampbell, M. (2022). Agricultural digitalization and automation in low- and middle-income countries: Evidence from ten case studies. FAO Agricultural Development Economics Technical Study No. 25. Food and Agriculture Organization of the United Nations. [Google Scholar] [Crossref]

10. Ministry of Economy Malaysia. (2025). Thirteenth Malaysia Plan, 2026–2030. Putrajaya: Ministry of Economy. [Google Scholar] [Crossref]

11. Ministry of Science, Technology and Innovation (MOSTI). (2023). Malaysia’s National Artificial Intelligence (AI) Roadmap 2021–2025. Putrajaya: MOSTI. [Google Scholar] [Crossref]

12. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. [Google Scholar] [Crossref]

13. Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books. [Google Scholar] [Crossref]

14. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. [Google Scholar] [Crossref]

15. Yusuf, M. S. A., Man, N., Haris, N. B. M., Ismail, I. A., Yee, S. S., & Abu Bakar, T. H. S. T. (2024). Improvement model framework of urban agriculture program in Malaysia: PLS-SEM analysis. Sarhad Journal of Agriculture, 40(1), 171–186. [Google Scholar] [Crossref]

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