Integration of IoT Technology and Social Factors in Sustainable Precision Agriculture Systems
Keywords:
Internet Of Things, Precision Agriculture, Social Factors, Smart Farming, Sustainable AgricultureAbstract
This study aims to analyze the integration of Internet of Things (IoT) technology and social factors in sustainable precision agriculture systems. The study responds to the growing need for data-driven farming while recognizing that digital transformation in agriculture depends on farmers’ experiences, trust, local knowledge, and institutional support. A qualitative case study approach was used to explore this phenomenon in a farming community where IoT-based tools had been introduced or used. Data were collected through semi-structured interviews, field observation, and documentation involving farmers, agricultural extension officers, farmer group leaders, technology providers, and local agricultural program managers. The data were analyzed using thematic analysis supported by data condensation, data display, and conclusion drawing. The findings reveal four main themes: perceived usefulness of IoT, trust in digital data, social support in technology use, and negotiation between local farming knowledge and digital recommendations. The study shows that farmers accept IoT when it offers practical benefits, supports existing farming judgment, and receives consistent technical and social support. These findings contribute to the understanding of precision agriculture as a socio-technical process rather than a purely technological intervention. The study implies that smart farming policies and programs should combine digital infrastructure, farmer mentoring, peer learning, and locally relevant training. Further research can compare different agricultural regions and crop systems to deepen understanding of social factors in IoT-based farming adoption.
References
Agussabti, A., Rahmaddiansyah, R., Hamid, A. H., Zakaria, Z., Munawar, A. A., & Bakar, B. A. (2022). Farmers’ perspectives on the adoption of smart farming technology to support food farming in Aceh Province, Indonesia. Open Agriculture, 7(1), 857–870. https://doi.org/10.1515/opag-2022-0145
Ahmed, S. K. (2024). The pillars of trustworthiness in qualitative research. Journal of Medicine, Surgery, and Public Health, 2, 100051. https://doi.org/10.1016/j.glmedi.2024.100051
Bolfe, É. L., Jorge, L. A. C., Sanches, I. D., Luchiari Júnior, A., Costa, C. C., Victoria, D. C., Inamasu, R. Y., Grego, C. R., Ferreira, V. R., & Ramirez, A. R. (2020). Precision and digital agriculture: Adoption of technologies and perception of Brazilian farmers. Agriculture, 10(12), 653. https://doi.org/10.3390/agriculture10120653
Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in reflexive thematic analysis? Qualitative Research in Sport, Exercise and Health, 13(2), 328–352. https://doi.org/10.1080/2159676X.2019.1628806
Byrne, D. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity, 56, 1391–1412. https://doi.org/10.1007/s11135-021-01182-y
Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10), 1745. https://doi.org/10.3390/agriculture12101745
Dibbern, T., Romani, L. A. S., Massruhá, S. M. F. S., & Padula, A. D. (2024). Main drivers and barriers to the adoption of digital agriculture technologies. Smart Agricultural Technology, 8, 100459. https://doi.org/10.1016/j.atech.2024.100459
Fragomeli, R., Nicotra, M., Romano, M., & Del Giudice, M. (2024). A systematic literature review on drivers and barriers of Agriculture 4.0 technologies adoption. Sustainability, 16(6), 2425. https://doi.org/10.3390/su16062425
Getahun, S., Kefale, H., & Gelaye, Y. (2024). Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. The Scientific World Journal, 2024, 2126734. https://doi.org/10.1155/2024/2126734
Giua, C., Materia, V. C., & Camanzi, L. (2022). Smart farming technologies adoption: Which factors play a role in the digital transition? Technology in Society, 68, 101869. https://doi.org/10.1016/j.techsoc.2022.101869
Karunathilake, E. M. B. M., Le, A. T., Heo, S., Chung, Y. S., & Mansoor, S. (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture, 13(8), 1593. https://doi.org/10.3390/agriculture13081593
Kendall, H., Naughton, P., Clark, B., Taylor, J., Li, Z., Zhao, C., Yang, G., & Chen, J. (2022). Precision agriculture technology adoption: A qualitative study of small-scale commercial family farms located in the North China Plain. Precision Agriculture, 23, 319–351. https://doi.org/10.1007/s11119-021-09839-2
Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical Teacher, 42(8), 846–854. https://doi.org/10.1080/0142159X.2020.1755030
Lim, W. M. (2025). What is qualitative research? An overview and guidelines. Australasian Marketing Journal, 33(2), 199–229. https://doi.org/10.1177/14413582241264619
Naeem, M., Ozuem, W., Howell, K., & Ranfagni, S. (2023). A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International Journal of Qualitative Methods, 22, 1–18. https://doi.org/10.1177/16094069231205789
Pandeya, S., Poudel, B., & Adhikari, R. (2025). Factors influencing precision agriculture technology adoption among small-scale farmers in Kentucky and their implications for policy and practice. Agriculture, 15(2), 177. https://doi.org/10.3390/agriculture15020177
Rejeb, A., Rejeb, K., Abdollahi, A., & Treiblmaier, H. (2022). The interplay between the Internet of Things and agriculture: A bibliometric analysis and research agenda. Internet of Things, 19, 100580. https://doi.org/10.1016/j.iot.2022.100580
Shang, L., Heckelei, T., Gerullis, M. K., Börner, J., & Rasch, S. (2021). Adoption and diffusion of digital farming technologies: Integrating farm-level evidence and system interaction. Agricultural Systems, 190, 103074. https://doi.org/10.1016/j.agsy.2021.103074
Talero-Sarmiento, L. H., Parra-Sánchez, D. T., & Lamos-Díaz, H. (2023). Opportunities and barriers of smart farming adoption by farmers based on a systematic literature review. INNODOCT/22 International Conference on Innovation, Documentation and Education Proceedings, 53–64. https://doi.org/10.4995/INN2022.2023.15746
Wang, Y., Xu, G., Cao, J., Chen, Y., & Wu, J. (2025). Does digital literacy affect farmers’ adoption of agricultural social services? An empirical study based on China Land Economic Survey data. PLOS ONE, 20(4), e0320318. https://doi.org/10.1371/journal.pone.0320318
Yeo, M. L., & Keske, C. M. (2024). From profitability to trust: Factors shaping digital agriculture adoption. Frontiers in Sustainable Food Systems, 8, 1456991. https://doi.org/10.3389/fsufs.2024.1456991
Zhang, X., Yang, Q., Al Mamun, A., Masukujjaman, M., & Masud, M. M. (2024). Acceptance of new agricultural technology among small rural farmers. Humanities and Social Sciences Communications, 11, 1641. https://doi.org/10.1057/s41599-024-04163-2
Zhahir, A. A., Wibowo, A., & Nugroho, H. (2024). Smart farming adoption: A scoping review. KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, 4(1), 1–13. https://doi.org/10.24002/konstelasi.v4i1.9257
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.




