DIGITALIZATION AND INNOVATION IN AGRICULTURAL PRODUCTIVITY: A NARRATIVE REVIEW OF TRANSFORMATIVE TECHNOLOGIES AND FUTURE DIRECTIONS

Authors

DOI:

https://doi.org/10.35631/AIJBES.829112

Keywords:

Agricultural Innovation, Agricultural Productivity, Artificial Intelligence In Agriculture

Abstract

This review explores how various digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), digital twins, and blockchain, impact and improve agricultural productivity. It also identifies barriers to the wide-scale implementation of these technologies. Literature was identified by a structured search of the Scopus database, and included peer-reviewed articles, book chapters, and proceeding publications, from 2023 to 2026. The publications were assessed by the review authors for inclusion based on whether the digital technologies described in the publications were linked to improvements in productivity or effectiveness. A narrative synthesis was performed based on 31 publications in this search, and supplemented by foundational publications. The literature was divided into four main groups. These were the use of IoT and Cyber-Physical Systems in farming, artificial intelligence in farming, blockchain and other technologies in farming and the supply chain, and barriers to the acceptance and adoption of farming technologies. The review identifies how digital tools help improve total factor productivity and the efficient use of resources within farms. However, the benefits are skewed where larger farms enjoy most of the benefits, while smaller farms continue to have challenges in adoption of digital tools. This is mostly attributed to capital and infrastructure constraints. More attention has been given to adopt individual digital technologies, compared to creation of a conducive innovation ecosystem to improve agricultural productivity. The review provides a framework to integrate the four key enablers and provide advice to stakeholders on ways to improve agricultural digitization in a sustainable and equitable manner.

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References

Abdurakhmonova, F., & Orzikulov, M. (2025). The overview of digital technologies on agricultural productivity: Analysis of current trends. Lecture Notes in Networks and Systems, 1337, 294–303. https://doi.org/10.1007/978-3-031-87532-8_26

Adrian, A. M., Dillard, C., & Mask, P. (2004). GIS in agriculture. In Geographic information systems in business (pp. 324–342). IGI Global. https://doi.org/10.4018/978-1-59140-399-9.ch015

Agrawal, K., & Kumar, N. (2025). AI-ML applications in agriculture and food processing. Sustainable Development Goals Series, Part F102, 21–37. https://doi.org/10.1007/978-3-031-76758-6_2

Ahirwar, S., Choubey, M., Gupta, Y. K., & Dubey, A. (2025). Digitization in smart farming: Revolutionizing agriculture. In AgriTech revolution: Next-gen solutions and challenges in modern agriculture (pp. 331–347). Springer. https://doi.org/10.1007/978-981-95-1268-3_17

Akimbekova, G., Espolov, T., Baimukhanov, A., Tazhibayeva, R., & Kontselidze, N. (2025). Digital transformation in agro-industrial complex: Technological innovations for sustainable development. Lecture Notes in Mechanical Engineering, 412–433. https://doi.org/10.1007/978-3-031-94223-5_36

Artykmyradov, B., Hojadurdyyev, H., Babayev, I., Kivulya, D., Klukin, A., & Begmatov, I. (2025). Digital technologies in agriculture: Benefits and costs at different levels. AIP Conference Proceedings, 3256(1), Article 050033. https://doi.org/10.1063/5.0267266

Babbar, S., Singhal, S., & Quraishi, S. J. (2025). Enhancing agricultural efficiency through the Internet of Things (IoT): Smart farming and precision agriculture. In Precision and intelligence in agriculture: Advanced technologies for sustainable farming (pp. 211–246). IGI Global. https://doi.org/10.4018/979-8-3373-5283-1.ch008

Bhanu, A. N., Vaibhav, B., & Raj, S. (2026). Challenges and opportunities using AI toward crop improvement. In Crop improvement with artificial intelligence: Methods and applications (pp. 437–449). Wiley. https://doi.org/10.1002/9781394330485.ch20

Bharat Babu, E., Ritvik, K., Sainath, L. V. A., & Tarun Sai, M. (2023). Blockchain-driven agricultural product traceability and supply chain management. In Proceedings of the 5th International Conference on Inventive Research in Computing Applications (ICIRCA 2023) (pp. 1202–1207). IEEE. https://doi.org/10.1109/ICIRCA57980.2023.10220840

Bhardwaj, S., Venkatesan, S., Rawat, S., & Nath, P. (2024). Transforming agriculture with IoT for precision agriculture and sustainable crop management. In The future of agriculture: IoT, AI and blockchain technology for sustainable farming (pp. 164–200). Bentham Science Publishers. https://doi.org/10.2174/9789815274349124010012

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10), Article 1745. https://doi.org/10.3390/agriculture12101745

Dong, J., & Xu, J. (2026). Driving agricultural strength through digital transformation. Scientific Reports, 16(1), Article 446. https://doi.org/10.1038/s41598-025-29878-3

Farzana Tasneem, M. I., & Achar, P. V. (2024). Journey to cyber-physical agricultural systems digitalization and technological evolution. In Agri 4.0 and the future of cyber-physical agricultural systems (pp. 1–18). Elsevier. https://doi.org/10.1016/B978-0-443-13185-1.00001-0

Geels, F. W. (2004). From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory. Research Policy, 33(6–7), 897–920. https://doi.org/10.1016/j.respol.2004.01.015

Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for peer-reviewed journals: Secrets of the trade. Journal of Chiropractic Medicine, 5(3), 101–117. https://doi.org/10.1016/S0899-3467(07)60142-6

Hajiyeva, M. (2025). The application and role of digital technologies in the agrarian sector in modern times. Scientific Work, 19(4), 220–224. https://doi.org/10.36719/2663-4619/114/220-224

Hiremath, G., & Hiremath, R. B. (2024). Transforming agri supply chains: A blockchain approach for quality assurance. In Sustaining the global agriculture supply chain (pp. 111–126). IGI Global. https://doi.org/10.4018/979-8-3693-4330-2.ch004

Hossain, S., & Ghosh, D. (2026). Emerging digital technologies in agriculture: Innovations for a sustainable future. In Digital twin technology for sustainable agriculture: Applications, implementation and future trends (pp. 37–55). Springer. https://doi.org/10.1007/978-981-95-5915-2_3

Jiménez-López, F. R., Vera-Cely, O. F., & Jiménez-López, A. F. (2025). Transforming agriculture with the application of the Internet of Agricultural Things (IoAT). In Proceedings of the LACCEI International Multi-Conference for Engineering, Education and Technology. https://doi.org/10.18687/LACCEI2025.1.1.1488

Kamal, S., & Saxena, S. (2026). Agriculture with IoT-enabled smart sensors: A new era of agriculture sustainability. Smart Innovation, Systems and Technologies, 467, 204–218. https://doi.org/10.1007/978-3-032-12983-3_21

Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037

Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS - Wageningen Journal of Life Sciences, 90–91, Article 100315. https://doi.org/10.1016/j.njas.2019.100315

Kumar, V., & Sharma, S. (2025). Artificial intelligence and machine learning use in agriculture domain: A review. In AI and ML techniques in image processing and object detection (pp. 29–50). Springer. https://doi.org/10.1007/978-981-96-7445-9_2

Mrudula, D., Pandit, V. B., Ravali, C., Fiaz, S., Tariq, A., & Chatterjee, S. (2026). The role of GIS and GPS in modern agricultural practices. In Artificial intelligence and data sciences for precision agriculture (pp. 59–83). Springer. https://doi.org/10.1007/978-3-032-12770-9_4

Postolache, S., Sebastião, P., Viégas, V., & Postolache, O. (2025). Instrumentation and measurement systems: Digital twin for horticulture farm: Data source and data domain architecture. IEEE Instrumentation & Measurement Magazine, 28(4), 22–30. https://doi.org/10.1109/MIM.2025.11021356

Priya, M. R., Rajan, R., Sathyanarayana, N., & Srikumar, M. S. S. V. (2026). Smart technologies for sustainable agriculture: Innovations and impacts within innovation ecosystems. In Handbook on integrating smart technologies for sustainable development (pp. 158–181). CRC Press. https://doi.org/10.1201/9781003586951-10

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Sankati, J., Bharti, P., Mishra, A. K., Durgam, V., Gakhar, S., & Sharma, S. (2025). Digital agriculture in South Asia: Innovations in farming for enhanced productivity. In Transition to regenerative agriculture: Principles and indicators of soil health management (pp. 111–140). Springer. https://doi.org/10.1007/978-981-96-1421-9_6

Sarma, S. S., Tholkapiyan, M., Shaik, N., Sundaram, A., Joshi, S., & Pathak, S. A. (2026). Remote sensing and GIS for precision agriculture: Enhancing crop monitoring and yield prediction. In Computational techniques in precision agriculture: Advances and applications (pp. 62–75). CRC Press. https://doi.org/10.1201/9781003520733-6

Sharma, S., & Tyagi, R. (2023). Digitalization of farming knowledge using artificial intelligence and Vedic scripture. In 3rd IEEE International Conference on ICT in Business Industry and Government (ICTBIG 2023). IEEE. https://doi.org/10.1109/ICTBIG59752.2023.10456219

Singh, G., & Veeramanickam, M. R. M. (2026). Applications of digital twins in agriculture: Enhancing crop monitoring, livestock health, soil fertility, and water management. In Digital twin technology for sustainable agriculture: Applications, implementation and future trends (pp. 295–307). Springer. https://doi.org/10.1007/978-981-95-5915-2_16

Singh, S., Singh, H. C., Gupta, P. K., Verma, A., & Verma, K. (2025). Embracing technological disruption: Digital tools for extension. In Global horizons in agricultural extension education: Insights from international perspectives (pp. 61–78).

Singh, V., & Shukla, P. (2026). Introduction to Agriculture 5.0. In Demystifying sustainable farming using smart technologies (pp. 1–16). CRC Press. https://doi.org/10.1201/9781779640932-1

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Sravani, M., & Veeranjaneyulu, N. (2025). A blockchain and IoT-driven framework for secure, transparent, and intelligent farm produce supply chains. In 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT 2025). IEEE. https://doi.org/10.1109/ICAFT66710.2025.11452710

Ugwu, O. P.-C., Ogenyi, F. C., Alum, E. U., Eze, V. H. U., Basajja, M., Ugwu, J. N., Ugwu, C. N., Ejemot-Nwadiaro, R. I., Okon, M. B., Egba, S. I., & Ejim, U. D. (2025). Implementing artificial intelligence and machine learning algorithms for optimized crop management: A systematic review on data-driven approach to enhancing resource use and agricultural sustainability. Cogent Food & Agriculture, 11(1), Article 2569982. https://doi.org/10.1080/23311932.2025.2569982

Vărzaru, A. A. (2025). Digital revolution in agriculture: Using predictive models to enhance agricultural performance through digital technology. Agriculture, 15(3), Article 258. https://doi.org/10.3390/agriculture15030258

Venable, C. (2008). Precision agricultural practices with GIS. GEO: Connexion, 7(5), 32–33.

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. https://doi.org/10.2307/30036540

Vignesh, B., Chandrakumar, M., Divya, K., Prahadeeswaran, M., & Vanitha, G. (2025). Blockchain technology in agriculture: Ensuring transparency and traceability in the food supply chain. Plant Science Today, 12. https://doi.org/10.14719/pst.5970

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023

World Bank. (2012). Agricultural innovation systems: An investment sourcebook. World Bank. https://doi.org/10.1596/978-0-8213-8684-2

Zhang, H., & Zhu, H. (2025). The impact of agricultural digitization on land productivity: An empirical test based on micro panel data. Land, 14(1), Article 187. https://doi.org/10.3390/land14010187

Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture – A worldwide overview. Computers and Electronics in Agriculture, 36(2–3), 113–132. https://doi.org/10.1016/S0168-1699(02)00096-0

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Published

2026-09-30

How to Cite

Annuar, A. Z. S., & Sidek, N. Z. M. (2026). DIGITALIZATION AND INNOVATION IN AGRICULTURAL PRODUCTIVITY: A NARRATIVE REVIEW OF TRANSFORMATIVE TECHNOLOGIES AND FUTURE DIRECTIONS. ADVANCED INTERNATIONAL JOURNAL OF BUSINESS, ENTREPRENEURSHIP AND SME’S (AIJBES), 8(29), 1955–1973. https://doi.org/10.35631/AIJBES.829112